Automated method of and system for identifying, measuring and enhancing categories of value for a value chain

ABSTRACT

An automated method and system ( 100 ) for identifying, measuring and enhancing categories of value for the different levels of a value chain on a continual basis. The categories of value are analyzed at each level in the value chain using predictive models and vector creation algorithms to define the enterprise and element vectors before valuing the organization, each enterprise in the organization and the elements of value in each enterprise. The relative strengths of the intangible elements of value are used in evaluating the real options of each enterprise and in determining the allocation of industry real options to the enterprise and the organization before summary reports are prepared, displayed and optionally printed. The system then generates potential value improvements which the user ( 20 ) optionally accepts, rejects or modifies before simulations are completed to analyze the value impact of the enhancements.

[0001] Application Ser. No. 09/295,337, filed Apr. 21, 1999, applicationSer. No. 09/293,336, filed Apr. 16, 1999, application Ser. No.09/135,983 filed Aug. 17, 1998, application Ser. No. 08/999,245, filedDec. 10, 1997 and application Ser. No. 08/779,109, filed Jan. 6, 1997which are also incorporated herein by reference. The subject matter ofthis application is also related to the subject matter of U.S. Pat. No.5,615,109 for “Method of and System for Generating Feasible, ProfitMaximizing Requisition Sets”, application Ser. No. 09/938,874 filed Aug.27, 2001, of application Ser. No. 09/761,671 filed Jan. 18, 2001, ofapplication Ser. No. 09/764,068 filed Jan. 19, 2001, of application Ser.No. 10/097,344 filed Mar. 13, 2002, of application Ser. No. 10/298,021filed Nov. 18, 2003, of application Ser. No, 10/282,113 filed Oct. 29,2002, of application Ser. No. 10/287,586 filed Nov. 5, 2002, ofapplication Ser. No. 10/283,083 filed Oct. 30, 2002, of application Ser.No. 10/441,385 filed May 20, 2003 and of application Ser. No. 10/674,861filed Aug. 25, 2003 by Jeff S. Eder, the disclosures of which are alsoincorporated herein by reference.

BACKGROUND OF THE INVENTION

[0002] This invention relates to a method of and system for businessvaluation, more particularly, to an automated system that identifies,evaluates and helps improve the management of the categories of valuefor a value chain and for each enterprise in the value chain on acontinual basis.

[0003] The internet has had many profound effects on global commerce.The dramatic increase in the use of email, the explosion of e-commerceand the meteoric rise in the market value of internet firms like eBay,Amazon.com and Yahoo! are some of the more visible examples of theimpact it has had on the American economy. Another impact of theinternet has been that it has enabled the “virtual integration” ofcompanies in different locations and different industries. Companies cannow join together in a matter of days with essentially no investment toform a “virtual value chain” for delivering products and services toconsumers.

[0004] The virtual value chain may appear to the consumer as a singleentity, when in reality a number of enterprises from differentcontinents have joined together to complete the preparation and deliveryof the good or service that is ultimately being purchased. Virtual valuechains allow each firm in the value chain to focus on their ownspecialty, be it manufacturing, design, distribution or marketing whilereaping the benefits of the increased scale and scope inherent in thealliance. Enabled by the low cost communication capability provided bythe internet, the virtual value chain is really just an extreme form ofa phenomenon that has been sweeping American industry for many years—theelectronic linkage of businesses.

[0005] Despite the widespread accceptance and use of “virtual valuechains” as a mechanism for efficiently and effectively responding tocustomer demands, there is no known method or system for systematicallyevaluating the value of these new types of organizations. In a similarmanner there is no known method or system for evaluating thecontribution of the different enterprises in the “virtual value chain”.

[0006] The need for a systematic approach for evaluating “virtual valuechains” is just part of a larger need that has recently appeared for anew method for systematically evaluating the financial performance of acommercial business. The need for a new approach has been highlighted inthe past two years by the multi-billion dollar valuations being placedon internet companies like Amazon.com, E trade and eBay that have neverearned a dollar of profit and that have no prospect of earning a dollarof profit any time soon. The most popular traditional approaches tovaluation are all based on some multiple of accounting earnings (a priceto earnings ratio or P/E ratio)—with no corporate earnings in the pastor the foreseeable future—these methods are of course useless inevaluating the new companies.

[0007] The inability of traditional methods to provide a framework foranalyzing “virtual value chains” and internet firms are just two glaringexamples of the weakness of traditional financial systems. Numerousacademic studies have demonstrated that accounting earnings don't fullyexplain changes in company valuations and the movement of stock prices.Many feel that because of this traditional accounting systems aredriving information-age managers to make the wrong decisions and thewrong investments. Accounting systems are “wrong” for one simple reason,they track tangible assets while ignoring intangible assets. Intangibleassets such as the skills of the workers, intellectual property,business infrastructure, databases, and relationships with customers andsuppliers are not measured with current accounting systems. Thisoversight is critical because in the present economy the success of anenterprise is determined more by its ability to use its intangibleassets than by its ability to amass and control the physical ones thatare tracked by traditional accounting systems.

[0008] Consultants from McKinsey & Company recently completed a threeyear study of companies in 10 industry segments in 12 countries thatconfirmed the importance of intangible assets as enablers of newbusiness expansion and profitable growth. The results of the study,published in the book The Alchemy of Growth, revealed three commoncharacteristics of the most successful businesses in today's economy:

[0009] 1. They consistently utilize “soft” or intangible assets likebrand names, customers and employees to support business expansion;

[0010] 2. They systematically generate and harvest real options forgrowth; and

[0011] 3. Their management focuses on 3 distinct “horizons”—short term(1-3 years), growth (3-5 years out) and options (beyond 5 years).

[0012] The experience of several of the most important companies in theU.S. economy, IBM, General Motors and DEC, in the late 1980's and early1990's illustrates the problems that can arise when intangible assetinformation is omitted from corporate financial statements and companiesfocus only on the short term horizon. All three companies were showinglarge profits using current accounting systems while their businesseswere deteriorating. If they had been forced to take write-offs when thedeclines in intangible assets were occurring, the problems would havebeen visible to the market and management would have been forced to actto correct the problems much more quickly than they actually did. Thesedeficiencies of traditional accounting systems are particularlynoticeable in high technology companies that are highly valued for theirintangible assets and their options to enter growing markets rather thantheir tangible assets.

[0013] The appearance of a new class of software applications, softasset management applications, is further evidence of the increasingimportance of “soft” or intangible assets. Soft asset managementapplications (or systems) include: alliance management systems, brandmanagement systems, customer relationship management systems, channelmanagement systems, intellectual property management systems, processmanagement systems and vendor management systems. While these systemsenhance the day to day management of the individual “soft” assets, thereis currently no mechanism for integrating the input from each of thesedifferent systems in to an overall organization or enterprise assetmanagement system. As a result, the organization or enterprise can be(and often is) faced with conflicting recommendations as each systemtries to optimize the asset it is focused on without considering theoverall financial performance of the organization or enterprise.

[0014] A number of people have suggested using business valuations inplace of traditional financial statements as the basis for measuring andmanaging financial performance. Unfortunately, using current methods,the valuation of a business is a complex and time-consuming undertaking.Business valuations determine the price that a hypothetical buyer wouldpay for a business under a given set of circumstances. The volume ofbusiness valuations being performed each year is increasingsignificantly. A leading cause of this growth in volume is theincreasing use of mergers and acquisitions as vehicles for corporategrowth. Business valuations are frequently used in setting the price fora business that is being bought or sold. Another reason for the growthin the volume of business valuations has been their increasing use inareas other than supporting merger and acquisition transactions. Forexample, business valuations are now being used by financialinstitutions to determine the amount of credit that should be extendedto a company, by courts in determining litigation settlement amounts andby investors in evaluating the performance of company management.

[0015] Income valuations are the most common type of valuation. They arebased on the premise that the current value of a business is a functionof the future value that an investor can expect to receive frompurchasing all or part of the business. In these valuations the expectedreturns from investing in the business and the risks associated withreceiving the expected returns are evaluated by the appraiser. Theappraiser then determines the value whereby a hypothetical buyer wouldreceive a sufficient return on the investment to compensate the buyerfor the risk associated with receiving the expected returns. Onedifficulty with this method is determining the lenth of time the companyis expected to generate the expected returns that drive the valuation.Most income valuations use an explicit forecast of returns for someperiod, usually 3 to 5 years, combined with a “residual”. The residualis generally a flat or uniformly growing forecast of future returns thatis discounted by some factor to estimate its value on the date ofvaluation. In some cases the residual is the largest part of thecalculated value.

[0016] One of the problems inherent in a steady state “residual”forecast is that returns don't continue forever. Economists generallyspeak of a competitive advantage period or CAP (hereinafter referred toas CAP) during which a given firm is expected to generate positivereturns. Under this theory, value is generated only during the CAP.After the CAP ends, value creation goes to zero or turns negative.Another change that has been produced by the internet economy is thatthe CAP for most businesses is generally thoght to be shrinking with theexception of companies whose products possess network externalities thattie others to the company and its products or services. These lattercompanies are thought to experience increasing returns as time goes byrather than having a finite CAP. Because the CAP is hard to calculate,it is generally ignored in income valuations however, the simplificationof ignoring the CAP greatly reduces the utility of the valuations thatare created with large residuals.

[0017] When performing a business valuation, the appraiser is generallyfree to select the valuation type and method (or some combination of themethods) in determining the business value. The usefulness of thesevaluations is limited because there is no correct answer, there is onlythe best possible informed guess for any given business valuation. Theusefulness of business valuations to business owners and managers isrestricted for another reason—valuations typically determine only thevalue of the business as a whole. To provide information that would beuseful in improving the business, the valuation would have to furnishsupporting detail that would highlight the value of different categoriesof value within the business. An operating manager would then be able touse a series of business valuations to identify categories within abusiness that have been decreasing in value. This information could alsobe used to help identify corrective action programs and to track theprogress that these programs have made in increasing business value.This same information could also be used to identify categories that arecontributing to an increase in business value. This information could beused to identify categories where increased levels of investment wouldhave a significant favorable impact on the overall health of thebusiness.

[0018] Even when intangible assets have been considered, the limitationsin the existing methodology have severely restricted the utility of thevaluations that have been produced. All known prior efforts to valueintangible assets have been restricted to independent valuations ofdifferent types of intangible assets (similar to the individual softasset management systems discussed previously). Intangible assets thathave been valued separately in this manner include: brand names,customers and intellectual property. Problems associated with existingmethods for valuing intangible assets include:

[0019] 1. interactions between the different intangible assets areignored,

[0020] 2. the actual impact of the asset on the enterprise isn'tmeasured,

[0021] 3. the relative strength of the intangible asset within theindustry is just as important (and in some cases more important) thanany absolute measure of its strength, and

[0022] 4. there is no systematic way for determining the life of theassets.

[0023] Typically, intangible asset valuations also ignore the realoptions for growth that are intimately inter-related and dependent uponthe intangible assets being evaluated. In addition to having a directinfluence on the valuation of a given real option the enterprise maypossess, intangible assets can affect the market's perception of whichcompany is likely to receive the lions share of future growth in a givenindustry. This, in turn affects the allocation of industry options tothe market price for equity in the enterprise.

[0024] The lack of a consistent, well accepted, realistic method formeasuring all the categories of business value also prevents some firmsfrom receiving the financing they need to grow. Most banks and lendinginstitutions focus on book value when evaluating the credit worthinessof a business seeking funds. As stated previously, the value of manyhigh technology firms lies primarily in intangible assets and realoptions that aren't visible under traditional definitions of accountingbook value. As a result, these businesses generally aren't eligible toreceive capital from traditional lending sources, even though theirfinancial prospects are generally far superior to those of companieswith much higher tangible book values.

[0025] In light of the preceding discussion, it is clear that it wouldbe advantageous to have an automated financial system that valued allthe assets and options for a given organization. Ideally, this systemwould be capable of generating detailed valuations for businesses in newindustries while prioritizing and coordinating the management of thedifferent soft assets that the organization is tracking.

SUMMARY OF THE INVENTION

[0026] It is a general object of the present invention to provide anovel and useful system that continuously calculates and displays acomprehensive and accurate valuation for all the categories of value fora virtual organization that overcomes the limitations and drawbacks ofthe existing art that were described previously.

[0027] A preferable object to which the present invention is applied isthe valuation and coordinated management of the different categories ofvalue within an organization that consists of two or more commercialenterprises that have come together to form a “virtual value chain” forthe purpose of delivering products or services to customers where alarge portion of the organization's business value is associated withintangibles and real options.

[0028] The present invention also provides the ability to calculate anddisplay a comprehensive and accurate valuation for the categories ofvalue for each commercial enterprise within the virtual value chain. Theability to “drill down” for more detailed analysis extends to eachelement of value within each enterprise in the “virtual value chain” asillustrated in Table 1. TABLE 1 Level Valuation Categories OrganizationCurrent Operation: Assets/Liabilities Current Operation: EnterpriseContribution & Joint: Real options/Contingent Liabilities EnterpriseCurrent Operation: Assets/Liabilities Current Operation: Elements ofValue Real Options/Contingent Liabilities & Market Sentiment Element ofValue Sub-elements of value

[0029] The present invention eliminates a great deal of time-consumingand expensive effort by automating the extraction of data from thedatabases, tables, and files of existing computer-based corporatefinance, operations, human resource and “soft” asset management systemdatabases as required to operate the system. In accordance with theinvention, the automated extraction, aggregation and analysis of datafrom a variety of existing computer-based systems significantlyincreases the scale and scope of the analysis that can be completed. Thesystem of the present invention further enhances the efficiency andeffectiveness of the business valuation by automating the retrieval,storage and analysis of information useful for valuing categories ofvalue from external databases and publications and the internet.Uncertainty over which method is being used for completing the valuationand the resulting inability to compare different valuations iseliminated by the present invention by consistently utilizing the sameset of valuation methodologies for valuing the different categories oforganization value as shown in Table 2. TABLE 2 Organization Categoriesof Value Valuation methodology Total current-operation value (COPTOT):Income Valuation Current Operation Cash & Marketable Securities GAAP forportion of assets/liabilities Assets/Liabilities: (CASH), Inventory(IN), from each enterprise that are devoted Accounts Receivable (AR), tothe organization Prepaid Expenses (PE), Other Assets (OA); AccountsPayable (AP), Notes Payable (NP), Other Liabilities (OL) CurentOperation Production Equipment Replacement Value for portion ofAssets/Liabilities: (PEQ), Other Physical Assets assets from eachenterprise that are (OPA) devoted to the organization Current OperationEnterprise contribution to System calculated value Enterprise virtualvalue chain (VVCC) Contribution: Current Operation General going concernGGCV = COPTOT − CASH − AR − IN − Enterprise element of value (GGCV) PE −PEQ − OPA − OA − VVCC Contribution: Real options/Contingent LiabilitiesReal option algorithms + allocation of industry real options based onrelative industry position

[0030] The present invention takes a similar approach to enterprisevalue analysis by consistently utilizing the same set of valuationmethodologies for valuing the different categories of enteprise value asshown in Table 3. TABLE 3 Enterprise Categories of Value Valuationmethodology Total current-operation value (COPTOT): Income ValuationCurrent-operation Cash & Marketable Securities GAAP Assets/Liabilities:(CASH), Inventory (IN), Accounts Receivable (AR), Prepaid Expenses (PE),Other Assets (OA), Accounts Payable (AP), Notes Payable (NP), OtherLiabilities (OL) Current-operation Production Equipment ReplacementValue Assets/Liabilities: (PEQ), Other Physical Assets (OPA) CurrentOperation Alliances, Brand Names, System calculated value Elements ofValue Channel Partners, (EV): Customers, Employees, Industry Factors*,Infrastructure, Intellectual Property, Information Technology, Processesand Vendors Current Operation General going concern GCV = COPTOT − CASH− AR − IN − Element of Value: (GCV) PE − PEQ − OPA − OA − ΣEV Realoptions/Contingent Liabilities Real option algorithms + allocation ofindustry real options based on relative strength of elements of value(EV) Market Sentiment Enterprise Market Value − (COPTOT + ΣReal optionValues)

[0031] There is no market sentiment calculation at the organizationlevel because the market value of each enterprise in the organizationgenerally includes non-value chain related activities and the firm levelmarket sentiment for each enterprise can not readily be sub-divided into value chain and non-value chain sentiment.

[0032] The market value of each enterprise in the organization iscalculated by adding the market value of all debt and equity as shown inTable 4. TABLE 4 Enterprise Market Value = Σ Market value of enterpriseequity + Σ Market value of company debt

[0033] One benefit of the novel system is that the market value of everyenterprise in the organization is subdivided in to at least threedistinct categories of value: current operation assets, elements ofvalue and real options. As shown in the table 5, these three valuecategories match the three distinct “horizons” for management focus theMcKinsey consultants reported on in The Alchemy of Growth. TABLE 5System Value Categories Three Horizons Current Operation Assets ShortTerm Elements of Value Growth Real Options Options

[0034] The utility of the valuations produced by the system of thepresent invention are further enhanced by explicitly calculating thelives of the different elements of value as required to remove theinaccuracy and distortion inherent in the use of a large residual.

[0035] As shown in Tables 2 and 3, growth opportunities and contingentliabilities are valued using real option algorithms. Because real optionalgorithms explicitly recognize whether or not an investment isreversible and/or if it can be delayed, the values calculated usingthese algorithms are more realistic than valuations created using moretraditional approaches like Net Present Value. The use of real optionanalysis for valuing growth opportunities and contingent liabilities(hereinafter, real options) gives the present invention a distinctadvantage over traditional approaches to business valuation.

[0036] The innovative system has the added benefit of providing a largeamount of detailed information concerning both tangible and intangibleelements of value. Because intangible elements are by definition nottangible, they can not be measured directly. They must instead bemeasured by the impact they have on their surrounding environment. Thereare analogies in the physical world. For example, electricity is an“intangible” that is measured by the impact it has on the surroundingenvironment. Specifically, the strength of the magnetic field generatedby the flow of electricity through a conductor is used to determine theamount of electricity that is being consumed. The system of the presentinvention measures intangible elements of value by identifying theattributes that, like the magnetic field, reflect the strength of theelement in driving the components of value (revenue, expense and changein capital) and are easy to measure. Once the attributes related to eachelement's strength are identified, they are summarized into a singleexpression (a composite variable or vector). The vectors for allelements are then evaluted to determine their relative contribution todriving each of the components of value. The system of the presentinvention calculates the product of each element's relative contributionand forecast life to determine the contribution to each of thecomponents of value. The contributions to each component of value arethen added together to determine the value of each element (see Table7).

[0037] The system also gives the user the ability to track the changesin categories of value by comparing the current valuations to previouslycalculated valuations. As such, the system provides the user with analternative to general ledger accounting systems for tracking financialperformance. To facilitate its use as a tool for improving the value ofa commercial enterprise, the system of the present invention producesreports in formats that are similar to the reports provided bytraditional accounting systems. The method for tracking the categoriesof value for a business enterprise provided by the present inventioneliminates many of the limitations associated with current accountingsystems that were described previously.

BRIEF DESCRIPTION OF DRAWINGS

[0038] These and other objects, features and advantages of the presentinvention will be more readily apparent from the following descriptionof the preferred embodiment of the invention in which:

[0039]FIG. 1 is a block diagram showing the major processing steps ofthe present invention;

[0040]FIG. 2 is a diagram showing the files or tables in the applicationdatabase of the present invention that are utilized for data storage andretrieval during the processing that values the categories of valuewithin the organization;

[0041]FIG. 3 is a block diagram of an implementation of the presentinvention;

[0042]FIG. 4 is a diagram showing the data windows that are used forreceiving information from and transmitting information to the user (20)during system processing;

[0043]FIG. 5A, FIG. 5B, FIG. 5C, FIG. 5D, FIG. 5E and FIG. 5F are blockdiagrams showing the sequence of steps in the present invention used forspecifying system settings and for initializing and operating the databots that extract, aggregate, store and manipulate information utilizedin system processing from: user input, the basic financial systemdatabase, the operation management system database, the human resourceinformation system database, external databases, the advanced financialsystem database, soft asset management system databases and theinternet;

[0044]FIG. 6A, FIG. 6B and FIG. 6C are block diagrams showing thesequence of steps in the present invention that are utilized forinitializing and operating the analysis bots;

[0045]FIG. 7 is a block diagram showing the sequence of steps in thepresent invention used for the analyzing enterprise market sentiment;

[0046]FIG. 8 is a block diagram showing the sequence of steps in thepresent invention used in trading organization stock and in preparing,displaying and optionally printing reports; and

[0047]FIG. 9 is a block diagram showing the sequence of steps in thepresent invention used for generating lists of value enhancing changesand calculating, displaying and optionally printing simulations of theeffects of user-specified and/or system generated changes in businessvalue drivers on the financial performance and the future value of theorganization and the enterprises in the organization;

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0048]FIG. 1 provides an overview of the processing completed by theinnovative system for business valuation. In accordance with the presentinvention, an automated method of and system (100) for businessvaluation is provided. Processing starts in this system (100) with a thespecification of system settings and the initialization and activationof software data “bots” (200) that extract, aggregate, manipulate andstore the data and user (20) input required for completing systemprocessing. This information is extracted via a network (45) from abasic financial system database (5), an operation management systemdatabase (10), a human resource information system database (15), anexternal database (25), an advanced financial system database (30), softasset management system databases (35) and the internet (40). Theseinformation extractions and aggregations may be influenced by a user(20) through interaction with a user-interface portion of theapplication software (700) that mediates the display, transmission andreceipt of all information to and from a browser (800) that the user(20) interacts with. While only one database of each type (5, 10, 15,25, 30 and 35) is shown in FIG. 1, it is to be understood that thesystem (100) can extract data from multiple databases of each type viathe network (45). The preferred embodiment of the present inventioncontains a soft asset management system for each element of value beinganalyzed. Automating the extraction and analysis of data from each softasset management system ensures that the management of each soft assetis considered and prioritized within the overall financial models forthe organization and for each enterprise in the organization. It shouldalso be understood that it is possible to complete a bulk extraction ofdata from each database (5, 10, 15, 25, 30 and 35) via the network (45)using data extraction applications such as Aclue from Decisionism andPower Center from Informatica before initializing the data bots. Thedata extracted in bulk could be stored in a single datamart ordatawarehouse where the data bots could operate on the aggregated data.

[0049] All extracted information is stored in a file or table(hereinafter, table) within an application database (50) as shown inFIG. 2. The application database (50) contains tables for storing userinput, extracted information and system calculations including a systemsettings table (140), a metadata mapping table (141), a conversion rulestable (142), a basic financial system table (143), an operation systemtable (144), a human resource system table (145), an external databasetable (146), an advanced finance system table (147), a soft asset systemtable (148), a bot date table (149), a keyword table (150), a classifiedtext table (151), a geospatial measures table (152), a compositevariables table (153), an industry ranking table (154), an element ofvalue definition table (155), a component of value definition table(156), a cluster ID table (157), an element variables table (158), avector table (159), a bot table (160), a cash flow table (161), a realoption value table (162), an enterprise vector table (163), a reporttable (164), an equity purchase table (165), an enterprise sentimenttable (166), a value driver change table (167), a simulation table (168)and a sentiment factors table (169). The application database (50) canoptionally exist as a datamart, data warehouse or departmentalwarehouse. The system of the present invention has the ability to acceptand store supplemental or primary data directly from user input, a datawarehouse or other electronic files in addition to receiving data fromthe databases described previously. The system of the present inventionalso has the ability to complete the necessary calculations withoutreceiving data from one or more of the specified databases. However, inthe preferred embodiment all required information is obtained from thespecified data sources (5, 10, 15, 25, 30, 35 and 40).

[0050] As shown in FIG. 3, the preferred embodiment of the presentinvention is a computer system (100) illustratively comprised of auser-interface personal computer (110) connected to an applicationserver personal computer (120) via a network (45). The applicationserver personal computer (120) is in turn connected via the network (45)to a database-server personal computer (130). The user interfacepersonal computer (110) is also connected via the network (45) to aninternet browser applicance (90) that contains browser software (800)such as Microsoft Internet Explorer or Netscape Navigator.

[0051] The database-server personal computer (130) has a read/writerandom access memory (131), a hard drive (132) for storage of theapplication database (50), a keyboard (133), a communications bus (134),a CRT display (135), a mouse (136), a CPU (137) and a printer (138).

[0052] The application-server personal computer (120) has a read/writerandom access memory (121), a hard drive (122) for storage of the nonuser interface portion of the application software (200, 300, 400, 500and 600) of the present invention, a keyboard (123), a communicationsbus (124), a CRT display (125), a mouse (126), a CPU (127) and a printer(128). While only one client personal computer is shown in FIG. 3, it isto be understood that the application-server personal computer (120) canbe networked to fifty or more client personal computers (110) via thenetwork (45). The application-server personal computer (120) can also benetworked to fifty or more server, personal computers (130) via thenetwork (45). It is to be understood that the diagram of FIG. 3 ismerely illustrative of one embodiment of the present invention.

[0053] The user-interface personal computer (110) has a read/writerandom access memory (111), a hard drive (112) for storage of a clientdata-base (49) and the user-interface portion of the applicationsoftware (700), a keyboard (113), a communications bus (114), a CRTdisplay (115), a mouse (116), a CPU (117) and a printer (118).

[0054] The application software (200, 300, 400, 500, 600 and 700)controls the performance of the central processing unit (127) as itcompletes the calculations required to calculate the detailed businessvaluation. In the embodiment illustrated herein, the applicationsoftware program (200, 300, 400, 500, 600 and 700) is written in acombination of C++ and Visual Basic®. The application software (200,300, 400, 500, 600 and 700) can use Structured Query Language (SQL) forextracting data from the databases and the internet (5, 10, 15, 25, 30,35 and 40). The user (20) can optionally interact with theuser-interface portion of the application software (700) using thebrowser software (800) in the browser appliance (90) to provideinformation to the application software (200, 300, 400, 500, 600 and700) for use in determining which data will be extracted and transferredto the application database (50) by the data bots.

[0055] User input is initially saved to the client database (49) beforebeing transmitted to the communication bus (125) and on to the harddrive (122) of the application-server computer via the network (45).Following the program instructions of the application software, thecentral processing unit (127) accesses the extracted data and user inputby retrieving it from the hard drive (122) using the random accessmemory (121) as computation workspace in a manner that is well known.

[0056] The computers (110, 120 and 130) shown in FIG. 3 illustrativelyare IBM PCs or clones or any of the more powerful computers orworkstations that are widely available. Typical memory configurationsfor client personal computers (110) used with the present inventionshould include at least 256 megabytes of semiconductor random accessmemory (111) and at least a 50 gigabyte hard drive (112). Typical memoryconfigurations for the application-server personal computer (120) usedwith the present invention should include at least 1028 megabytes ofsemiconductor random access memory (121) and at least a 100 gigabytehard drive (122). Typical memory configurations for the database-serverpersonal computer (130) used with the present invention should includeat least 2056 megabytes of semiconductor random access memory (135) andat least a 500 gigabyte hard drive (131).

[0057] Using the system described above, the value of the organiztion,each enterprise within the organization and each element of value can bebroken down into the value categories listed in Table 1. As shown inTable 2 and Table 3, the value of the current-operation will becalculated using an income valuation. An integral part of most incomevaluation models is the calculation of the present value of the expectedcash flows, income or profits associated with the current-operation. Thepresent value of a stream of cash flows is calculated by discounting thecash flows at a rate that reflects the risk associated with realizingthe cash flow. For example, the present value (PV) of a cash flow of tendollars ($10) per year for five (5) years would vary depending on therate used for discounting future cash flows as shown below. Discountrate = 25% PV = 10 + 10 + 10 + 10 + 10 = 26.89 {overscore (1.25)}{overscore ((1.25))}² {overscore ((1.25))}³ {overscore ((1.25))}⁴{overscore ((1.25))}⁵ Discount rate = 35% PV = 10 + 10 + 10 + 10 + 10 =22.20 {overscore (1.35)} {overscore ((1.35))}² {overscore ((1.35))}³{overscore ((1.35))}⁴ {overscore ((1.35))}⁵

[0058] One of the first steps in evaluating the elements ofcurrent-operation value is extracting the data required to completecalculations in accordance with the formula that defines the value ofthe current-operation as shown in Table 6. TABLE 6 Value of curr nt-opration = (R) Value of forecast revenue from current-operation(positive) + (E) Value of forecast expense for current-operation(negative) + (C)* Value of current operation capital change forecast

[0059] The three components of current-operation value will be referredto as the revenue value (R), the expense value (E) and the capital value(C). Examination of the equation in Table 6 shows that there are threeways to increase the value of the current-operation—increase therevenue, decrease the expense or decrease the capital requirements(note: this statement ignores a fourth way to increase value—decreaseinterest rate used for discounting future cash flows).

[0060] In the preferred embodiment, the revenue, expense and capitalrequirement forecasts for the current operation, the real options andthe contingent liabilities are obtained from an advanced financialplanning system database (30) from an advanced financial planning systemsimilar to the one disclosed in U.S. Pat. No. 5,615,109. The extractedrevenue, expense and capital requirement forecasts are used to calculatea cash flow for each period covered by the forecast for the organizationand each enterprise in the organization by subtracting the expense andchange in capital for each period from the revenue for each period. Asteady state forecast for future periods is calculated after determiningthe steady state growth rate the best fits the calculated cash flow forthe forecast time period. The steady state growth rate is used tocalculate an extended cash flow forecast. The extended cash flowforecast is used to determine the Competitive Advantage Period (CAP)implicit in the enteprise market value.

[0061] While it is possible to use analysis bots to sub-divide each ofthe components of current operation value into a number ofsub-components for analysis, the preferred embodiment has apre-determined number of sub-components for each component of value forthe organization and each enterprise in the organization. The revenuevalue is not subdivided. In the preferred embodiment, the expense valueis subdivided into five sub-components: the cost of raw materials, thecost of manufacture or delivery of service, the cost of selling, thecost of support and the cost of administration. The capital value issubdivided into six sub-components: cash, non-cash financial assets,production equipment, other assets (non financial, non productionassets), financial liabilities and equity. The production equipment andequity sub-components are not used directly in evaluating the elementsof value.

[0062] The components and sub-components of current-operation value willbe used in calculating the value of: enteprise contribution, elements ofvalue and sub-elements of value. Enterprise contribution will be definedas “the economic benefit that as a result of past transactions anenterprise is expected to provide to an organization.” In a similarfashion, an element of value will be defined as “an identifiable entityor group of items that as a result of past transactions has provided andis expected to provide economic benefit to an enterprise”. An item willbe defined as a single member of the group that defines an element ofvalue. For example, an individual salesman would be an “item” in the“element of value” sales staff. The data associated with performance ofan individual item will be referred to as “item variables”.

[0063] Analysis bots are used to determine enterprise and element ofvalue lives and the percentage of: the revenue value, the expense value,and the capital value that are attributable to each element of value.The resulting values are then be added together to determine thevaluation for different elements as shown by the example in Table 7.TABLE 7 Percent- Element Gross Value age Life/CAP Net Value Revenuevalue = $120 M 20% 80% Value = $19.2 M Expense value = ($80 M) 10% 100% Value = ($8.0) M Capital value = ($5 M)  5% 80% Value = ($0.2) M Totalvalue = $35 M Net value for this element: Value = $11.0 M

[0064] The valuation of an organization and the enterprises in theorganization using the approach outlined above is completed in fivedistinct stages. As shown in FIG. 5A, FIG. 5B, FIG. 5C, FIG. 5D, FIG. 5Eand FIG. 5F the first stage of processing (block 200 from FIG. 1)programs bots to continually extract, aggregate, manipulate and storethe data from user input and databases and the internet (5, 10, 15, 25,30, 35 or 40) as required for the analysis of business value. Bots areindependent components of the application that have specific tasks toperform. As shown in FIG. 6A, FIG. 6B and FIG. 6C the second stage ofprocessing (block 300 from FIG. 1) programs analysis bots tocontinually:

[0065] 1. identify the item variables, item performance indicators andcomposite variables for each enterprise, element of value andsub-element of value that drive the components of value (revenue,expense and changes in capital),

[0066] 2. create vectors that summarize the performance of the itemvariables and item performance indicators for each enterprisecontribution, element of value and sub-element of value,

[0067] 3. determine the appopriate cost of capital and value theorganization and enteprise real options;

[0068] 4. determine the appopriate cost of capital, value and allocatethe industry real options to each organization or enterprise on thebasis of relative element strength;

[0069] 5. determine the expected life of each element of value andsub-element of value;

[0070] 6. calculate the organization and enterprise current operationvalues and value the revenue, expense and capital components saidcurrent operations using the information prepared in the previous stageof processing;

[0071] 7. specify and optimize predictive models to determine therelationship between the vectors determined in step 2 and the revenue,expense and capital values determined in step 6,

[0072] 8. combine the results of the fifth, sixth and seventh stages ofprocessing to determine the value of each, enterprise contribution,element and sub-element (as shown in Table 7);

[0073] The third stage of processing (block 400 from FIG. 1) analyzesthe market sentiment associated with each enterprise as shown in FIG. 7.The fourth stage of processing (block 500 from FIG. 1) displays theresults of the prior calculations in specified formats and optionallygenerates trades in enterprise stock as shown in FIG. 8. The fifth andfinal stage of processing (block 600 from FIG. 1) identifies potentialimprovements in organization and enterprise operation and analyzes theimpact of proposed improvements on financial performance and businessvalue for the organization and each enterprise as shown in FIG. 9.

SYSTEM SETTINGS AND DATA BOTS

[0074] The flow diagrams in FIG. 5A, FIG. 5B, FIG. 5C, FIG. 5D, FIG. 5Eand FIG. 5F detail the processing that is completed by the portion ofthe application software (200) that extracts, aggregates, transforms andstores the information required for system operation from: the basicfinancial system database (5), operation management system database(10), human resource information system database (15), external database(25), advanced financial system database (30), soft asset managementsystem database (35), the internet (40) and the user (20). A briefoverview of the different databases will be presented before reviewingeach step of processing completed by this portion (200) of theapplication software.

[0075] Corporate financial software systems are generally divided intotwo categories, basic and advanced. Advanced financial systems utilizeinformation from the basic financial systems to perform financialanalysis, financial planning and financial reporting functions.Virtually every commercial enterprise uses some type of basic financialsystem as they are required to use these systems to maintain books andrecords for income tax purposes. An increasingly large percentage ofthese basic financial systems are resident in microcomputer andworkstation systems. Basic financial systems include general-ledgeraccounting systems with associated accounts receivable, accountspayable, capital asset, inventory, invoicing, payroll and purchasingsubsystems. These systems incorporate worksheets, files, tables anddatabases. These databases, tables and files contain information aboutthe company operations and its related accounting transactions. As willbe detailed below, these databases, tables and files are accessed by theapplication software of the present invention as required to extract theinformation required for completing a business valuation. The system isalso capable of extracting the required information from a datawarehouse (or datamart) when the required information has beenpre-loaded into the warehouse.

[0076] General ledger accounting systems generally store only validaccounting transactions. As is well known, valid accounting transactionsconsist of a debit component and a credit component where the absolutevalue of the debit component is equal to the absolute value of thecredit component. The debits and the credits are posted to the separateaccounts maintained within the accounting system. Every basic accountingsystem has several different types of accounts. The effect that theposted debits and credits have on the different accounts depends on theaccount type as shown in Table 8. TABLE 8 Account Type: Debit Impact:Credit Impact: Asset Increase Decrease Revenue Decrease Increase ExpenseIncrease Decrease Liability Decrease Increase Equity Decrease Increase

[0077] General ledger accounting systems also require that the assetaccount balances equal the sum of the liability account balances andequity account balances at all times.

[0078] The general ledger system generally maintains summary, dollaronly transaction histories and balances for all accounts while theassociated subsystems, accounts payable, accounts receivable, inventory,invoicing, payroll and purchasing, maintain more detailed historicaltransaction data and balances for their respective accounts. It iscommon practice for each subsystem to maintain the detailed informationshown in Table 9 for each transaction. TABLE 9 Subsystem DetailedInformation Accounts Vendor, Item(s), Transaction Date, Amount PayableOwed, Due Date, Account Number Accounts Customer, Transaction Date,Product Sold, Receivable Quantity, Price, Amount Due, Terms, Due Date,Account Number Capital Asset ID, Asset Type, Date of Purchase, AssetsPurchase Price, Useful Life, Depreciation Schedule, Salvage ValueInventory Item Number, Transaction Date, Transaction Type, TransactionQty, Location, Account Number Invoicing Customer Name, Transaction Date,Item(s) Sold, Amount Due, Due Date, Account Number Payroll EmployeeName, Employee Title, Pay Frequency, Pay Rate, Account Number PurchasingVendor, Item(s), Purchase Quantity, Purchase Price(s), Due Date, AccountNumber

[0079] As is well known, the output from a general ledger systemincludes income statements, balance sheets and cash flow statements inwell defined formats which assist management in measuring the financialperformance of the firm during the prior periods when data input andsystem processing have been completed.

[0080] While basic financial systems are similar between firms,operation management systems vary widely depending on the type ofcompany they are supporting. These systems typically have the ability tonot only track historical transactions but to forecast futureperformance. For manufacturing firms, operation management systems suchas Enterprise Resource Planning Systems (ERP), Material RequirementPlanning Systems (MRP), Purchasing Systems, Scheduling Systems andQuality Control Systems are used to monitor, coordinate, track and planthe transformation of materials and labor into products. Systems similarto the one described above may also be useful for distributors to use inmonitoring the flow of products from a manufacturer.

[0081] Operation Management Systems in manufacturing firms may alsomonitor information relating to the production rates and the performanceof individual production workers, production lines, work centers,production teams and pieces of production equipment including theinformation shown in Table 10. TABLE 10 Operation Management System -Production Information 1. ID number (employee id/machine id) 2. Actualhours - last batch 3. Standard hours - last batch 4. Actual hours - yearto date 5. Actual/Standard hours - year to date % 6. Actual setup time -last batch 7. Standard setup time - last batch 8. Actual setup hours -year to date 9. Actual/Standard setup hrs - yr to date % 10. Cumulativetraining time 11. Job(s) certifications 12. Actual scrap - last batch13. Scrap allowance - last batch 14. Actual scrap/allowance - year todate 15. Rework time/unit last batch 16. Rework time/unit year to date17. QC rejection rate - batch 18. QC rejection rate - year to date

[0082] Operation management systems are also useful for trackingrequests for service to repair equipment in the field or in acentralized repair facility. Such systems generally store informationsimilar to that shown below in Table 11. TABLE 11 Operation ManagementSystem - Service Call Information 1. Customer name 2. Customer number 3.Contract number 4. Service call number 5. Time call received 6.Product(s) being fixed 7. Serial number of equipment 8. Name of personplacing call 9. Name of person accepting call 10. Promised response time11. Promised type of response 12. Time person dispatched to call 13.Name of person handling call 14. Time of arrival on site 15. Time ofrepair completion 16. Actual response type 17. Part(s) replaced 18.Part(s) repaired 19. 2nd call required 20. 2nd call number

[0083] Computer based human resource systems may some times be packagedor bundled within enterprise resource planning systems such as thoseavailable from SAP, Oracle and Peoplesoft. Human resource systems areincreasingly used for storing and maintaining corporate recordsconcerning active employees in sales, operations and the otherfunctional specialties that exist within a modern corporation. Storingrecords in a centralized system facilitates timely, accurate reportingof overall manpower statistics to the corporate management groups andthe various government agencies that require periodic updates. In somecases human resource systems include the company payroll system as asubsystem. In the preferred embodiment of the present invention, thepayroll system is part of the basic financial system. These systems canalso be used for detailed planning regarding future manpowerrequirements. Human resource systems typically incorporate worksheets,files, tables and databases that contain information about the currentand future employees. As will be detailed below, these databases, tablesand files are accessed by the application software of the presentinvention as required to extract the information required for completinga business valuation. It is common practice for human resource systemsto store the information shown in Table 12 for each employee. TABLE 12Human Resource System Information 1. Employee name 2. Job title 3. Jobcode 4. Rating 5. Division 6. Department 7. Employee No./(SocialSecurity Number) 8. Year to date - hours paid 9. Year to date - hoursworked 10. Employee start date - company 11. Employee start date -department 12. Employee start date - current job 13. Training coursescompleted 14. Cumulative training expenditures 15. Salary history 16.Current salary 17. Educational background 18. Current supervisor

[0084] External databases can be used for obtaining information thatenables the definition and evaluation of a variety of things includingelements of value, sentiment factors, industry real options andcomposite variables. In some cases information from these databases canbe used to supplement information obtained from the other databases andthe internet (5, 10, 15, 30, 35 and 40). In the system of the presentinvention, the information extracted from external databases (25) can bein the forms listed in Table 13. Types of information a) numericinformation such as that found in the SEC Edgar database and thedatabases of financial infomediaries such as FirstCall, IBES andCompustat, b) text information such as that found in the Lexis Nexisdatabase and databases containing past issues from specificpublications, c) multimedia information such as video and audio clips,and d) geospatial data.

[0085] The system of the present invention uses different “bot” types toprocess each distinct data type from external databases (25). The same“bot types” are also used for extracting each of the different types ofdata from the internet (40). The system of the present invention musthave access to at least one external database (25) that providesinformation regarding the equity prices for each enterprise in theorganization and the equity prices and financial performance ofcompetitors.

[0086] Advanced financial systems may also use information from externaldatabases (25) and the internet (40) in completing their processing.Advanced financial systems include financial planning systems andactivity based costing systems. Activity based costing systems may beused to supplement or displace the operation of the expense componentanalysis segment of the present invention as disclosed previously.Financial planning systems generally use the same format used by basicfinancial systems in forecasting income statements, balance sheets andcash flow statements for future periods. Management uses the output fromfinancial planning systems to highlight future financial difficultieswith a lead time sufficient to permit effective corrective action and toidentify problems in company operations that may be reducing theprofitability of the business below desired levels. These systems aremost often developed by individuals within companies using 2 and 3dimensional spreadsheets such as Lotus 1-2-3 ®, Microsoft Excel® andQuattro Pro®. In some cases, financial planning systems are built withinan executive information system (EIS) or decision support system (DSS).For the preferred embodiment of the present invention, the advancedfinancial system database is similar to the financial planning systemdatabase detailed in U.S. Pat. No. 5,165,109 for “Method of and Systemfor Generating Feasible, Profit Maximizing Requisition Sets”, by Jeff S.Eder, the disclosure of which is incorporated herein by reference.

[0087] While advanced financial planning systems have been around forsome time, soft asset management systems are a relatively recentdevelopment. Their appearance is further proof of the increasingimportance of “soft” assets. Soft asset management systems include:alliance management systems, brand management systems, customerrelationship management systems, channel management systems,intellectual property management systems, process management systems andvendor management systems. Soft asset management systems are similar tooperation management systems in that they generally have the ability toforecast future events as well as track historical occurrences. Customerrelationship management systems are the most well established soft assetmanagement systems at this point and will the focus of the discussionregarding soft asset management system data. In firms that sellcustomized products, the customer relationship management system isgenerally integrated with an estimating system that tracks the flow ofestimates into quotations, orders and eventually bills of lading andinvoices. In other firms that sell more standardized products, customerrelationship management systems generally are used to track the salesprocess from lead generation to lead qualification to sales call toproposal to acceptance (or rejection) and delivery. All customerrelationship management systems would be expected to track all of thecustomer's interactions with the enterprise after the first sale andstore information similar to that shown below in Table 14. TABLE 14Customer Relationship Management System - Information 1.Customer/Potential customer name 2. Customer number 3. Address 4. Phonenumber 5. Source of lead 6. Date of first purchase 7. Date of lastpurchase 8. Last sales call/contact 9. Sales call history 10. Salescontact history 11. Sales history: product/qty/price 12. Quotations:product/qty/price 13. Custom product percentage 14. Payment history 15.Current A/R balance 16. Average days to pay

[0088] System processing of the information from the different databasesand the internet (5, 10, 15, 25, 30, 35 and 40) described above startsin a block 201, FIG. 5A, which immediately passes processing to asoftware block 202. The software in block 202 prompts the user (20) viathe system settings data window (701) to provide system settinginformation. The system setting information entered by the user (20) istransmitted via the network (45) back to the application server (120)where it is stored in the system settings table (140) in the applicationdatabase (50) in a manner that is well known. The specific inputs theuser (20) is asked to provide at this point in processing are shown inTable 15. TABLE 15 1. New run or structure revision? 2. Continuous, Ifyes, frequency? (hourly, daily, weekly, monthly or quarterly) 3.Structure of virtual organization (organization, enterprises andsub-elements) 4. Organization checklist 5. Enterprise checklist 6. Baseacount structure 7. Metadata standard (XML, MS OIM, MDC) 8. Location ofbasic financial system database and metadata 9. Location of advancedfinancial system database and metadata 10. Location of human resourceinformation system database and metadata 11. Location of operationmanagement system database and metadata 12. Location of soft assetmanagement system databases and metadata 13. Location of externaldatabase and metadata 14. Location of account structure 15. Basecurrency 16. Location of database and metadata for equity information17. Location of database and metadata for debt information 18. Locationof database and metadata for tax rate information 19. Location ofdatabase and metadata for currency conversion rate information 20.Geospatial data? If yes, identity of geocoding service. 21. The maximumnumber of generations to be processed without improving fitness 22.Default clustering algorithm (selected from list) and maximum clusternumber 23. Amount of cash and marketable securities required for day today operations 24. Weighted average cost of capital (optional input) 25.Number of months a product is considered new after it is first produced26. Organization industry segments (SIC Code) 27. Enterprise industrysegments (SIC Code) 28. Primary competitors by industry segment 29.Management report types (text, graphic, both) 30. Default reports 31.Trading in enterprise equity authorized? 32. On-line equity tradingaccount information 33. Default Missing Data Procedure 34. Maximum timeto wait for user input

[0089] The organization and enterprise checklists are used by a “rules”engine (such as the one available from Neuron Data) in block 202 toinfluence the number and type of items with pre-defined metadata mappingfor each category of value. For example, if the checklists indicate thatthe organization and enterprises are focused on branded, consumermarkets, then additional brand related factors will be pre-defined formapping. The application of these system settings will be furtherexplained as part of the detailed explanation of the system operation.

[0090] The software in block 202 also uses the current system date todetermine the time periods (months) that require data in order tocomplete the current operation and the real option valuations and storesthe resulting date range in the system settings table (140). In thepreferred embodiment the valuation of the current operation by thesystem utilizes basic finance, advanced financial, soft assetmanagement, external database and human resource data for the three yearperiod before and the three year forecast period after the current date.

[0091] After the storage of system setting data is complete, processingadvances to a software block 203. The software in block 203 prompts theuser (20) via the metadata and conversion rules window (702) to mapmetadata using the standard specified by the user (20) (XML, Microsoft'sOpen Information Model of the Metadata Coalitions specification) fromthe basic financial system database (5), the operation management systemdatabase (10), the human resource information system database (15), theexternal database (25), the advanced financial system database (30) andthe soft asset management system database (35) to the organizationalhierarchy stored in the system settings table (140) and to thepre-specified fields in the metadata mapping table (141). Pre-specifiedfields in the metadata mapping table include, the revenue, expense andcapital components and sub-components for the organization and eachenterprise and pre-specified fields for expected value drivers. Becausethe bulk of the information being extracted is financial information,the metadata mapping often takes the form of specifying the accountnumber ranges that correspond to the different fields in the metadatamapping table (141). Table 16, shows the base account number structurethat the account numbers in the other systems must align with. Forexample, using the structure shown below, the revenue component for theorganization could be specified as organization 01, any enterprisenumber, any deparment number, accounts 400 to 499 (the revenue accountrange) with any sub-account. TABLE 16 Account Number 01 - 800 - 901 -677- 003 Segment Organi- Enterprise Department Account Sub- zationaccount Subgroup Products Workstation Marketing Labor P.R. Position 5 43 2 1

[0092] As part of the metadata mapping process, any database fields thatare not mapped to pre-specified fields are defined by the user (20) ascomponent of value. elements of value or non-relevant attributes and“mapped” in the metadata mapping table (141) to the corresponding fieldsin each database in a manner identical to that described above for thepre-specified fields. After all fields have been mapped to the metadatamapping table (141), the software in block 203 prompts the user (20) viathe metadata and conversion rules window (702) to provide conversionrules for each metadata field for each data source. IConversion ruleswill include information regarding currency conversions and conversionfor units of measure that may be required to accurately and consistentlyanalyze the data. The inputs from the user (20) regarding conversionrules are stored in the conversion rules table (142) in the applicationdatabase. When conversion rules have been stored for all fields fromevery data source, then processing advances to a software block 204.

[0093] The software in block 204 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 212. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 207.

[0094] The software in block 207 checks the bot date table (149) anddeactivates any basic financial system data bots with creation datesbefore the current system date and retrieves information from the systemsetting table (140), metadata mapping table (141) and conversion rulestable (142). The software in block 207 then initializes data bots foreach field in the metadata mapping table (141) that mapped to the basicfinancial system database (5) in accordance with the frequency specifiedby user (20) in the system settings table (140). Bots are independentcomponents of the application that have specific tasks to perform. Inthe case of data acquisition bots, their tasks are to extract andconvert data from a specified source and then store it in a specifiedlocation. Each data bot initialized by software block 207 will store itsdata in the basic financial system table (143). Every data acquisitionbot for every data source contains the information shown in Table 17.TABLE 17 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. The data source location 3. Mapping information 4. Timingof extraction 5. Conversion rules (if any) 6. Storage Location (to allowfor tracking of source and destination events) 7. Creation date (day,hour, minute, second)

[0095] After the software in block 207 initializes all the bots for thebasic financial system database, processing advances to a block 208. Inblock 208, the bots extract and convert data in accordance with theirpreprogrammed instructions in accordance with the frequency specified byuser (20) in the system settings table (140). As each bot extracts andconverts data from the basic financial system database (5), processingadvances to a software block 209 before the bot completes data storage.The software in block 209 checks the basic financial system metadata tosee if all fields have been extracted. If the software in block 209finds no unmapped data fields, then the extracted, converted data isstored in the basic financial system table (143). Alternatively, ifthere are fields that haven't been extracted, then processing advancesto a block 210. The software in block 210 prompts the user (20) via themetadata and conversion rules window (702) to provide metadata andconversion rules for each new field. The information regarding the newmetadata and conversion rules is stored in the metadata mapping table(141) and conversion rules table (142) while the extracted, converterddata is stored in the basic financial system table (143). It is worthnoting at this point that the activation and operation of bots thatdon't have unmapped fields continues. Only bots with unmapped fields“wait” for user input before completing data storage. The new metadataand conversion rule information will be used the next time bots areinitialized in accordance with the frequency established by the user(20). In either event, system processing passes, on to software block212.

[0096] The software in block 212 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 224. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 221.

[0097] The software in block 221 checks the bot date table (149) anddeactivates any operations management system data bots with creationdates before the current system date and retrieves information from thesystem setting table (140), metadata mapping table (141) and conversionrules table (142). The software in block 221 then initializes data botsfor each field in the metadata mapping table (141) that mapped to theoperations management system database (10) in accordance with thefrequency specified by user (20) in the system settings table (140).Each data bot initialized by software block 221 will store its data inthe operations system table (144).

[0098] After the software in block 221 initializes all the bots for theoperations management system database, processing advances to a block222. In block 222, the bots extract and convert data in accordance withtheir preprogrammed instructions in accordance with the frequencyspecified by user (20) in the system settings table (140). As each botextracts and converts data from the operations management systemdatabase (10), processing advances to a software block 209 before thebot completes data storage. The software in block 209 checks theoperations management system metadata to see if all fields have beenextracted. If the software in block 209 finds no unmapped data fields,then the extracted, converted data is stored in the operations systemtable (144). Alternatively, if there are fields that haven't beenextracted, then processing advances to a block 210. The software inblock 210 prompts the user (20) via the metadata and conversion ruleswindow (702) to provide metadata and conversion rules for each newfield. The information regarding the new metadata and conversion rulesis stored in the metadata mapping table (141) and conversion rules table(142) while the extracted, converterd data is stored in the operationssystem table (144). It is worth noting at this point that the activationand operation of bots that don't have unmapped fields continues. Onlybots with unmapped fields “wait” for user input before completing datastorage. The new metadata and conversion rule information will be usedthe next time bots are initialized in accordance with the frequencyestablished by the user (20). In either event, system processing thenpasses, on to software block 224.

[0099] The software in block 224 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 228. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 225.

[0100] The software in block 225 checks the bot date table (149) anddeactivates any human resource management system data bots with creationdates before the current system date and retrieves information from thesystem setting table (140), metadata mapping table (141) and conversionrules table (142). The software in block 225 then initializes data botsfor each field in the metadata mapping table (141) that mapped to thehuman resource management system database (15) in accordance with thefrequency specified by user (20) in the system settings table (140).Each data bot initialized by software block 225 will store its data inthe human resource system table (145).

[0101] After the software in block 225 initializes all the bots for thehuman resource management system database, processing advances to ablock 226. In block 226, the bots extract and convert data in accordancewith their preprogrammed instructions in accordance with the frequencyspecified by user (20) in the system settings table (140). As each botextracts and converts data from the human resource management systemdatabase (15), processing advances to a software block 209 before thebot completes data storage. The software in block 209 checks the humanresource management system metadata to see if all fields have beenextracted. If the software in block 209 finds no unmapped data fields,then the extracted, converted data is stored in the human resourcesystem table (145). Alternatively, if there are fields that haven't beenextracted, then processing advances to a block 210. The software inblock 210 prompts the user (20) via the metadata and conversion ruleswindow (702) to provide metadata and conversion rules for each newfield. The information regarding the new metadata and conversion rulesis stored in the metadata mapping table (141) and conversion rules table(142) while the extracted, converterd data is stored in the humanresource system table (145). It is worth noting at this point that theactivation and operation of bots that don't have unmapped fieldscontinues. Only bots with unmapped fields “wait” for user input beforecompleting data storage. The new metadata and conversion ruleinformation will be used the next time bots are initialized inaccordance with the frequency established by the user (20). In eitherevent, system processing then passes, on to software block 228.

[0102] The software in block 228 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 244. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 241.

[0103] The software in block 241 checks the bot date table (149) anddeactivates any external database data bots with creation dates beforethe current system date and retrieves information from the systemsetting table (140), metadata mapping table (141) and conversion rulestable (142). The software in block 241 then initializes data bots foreach field in the metadata mapping table (141) that mapped to theexternal database (25) in accordance with the frequency specified byuser (20) in the system settings table (140). Each data bot initializedby software block 241 will store its data in the external database table(146).

[0104] After the software in block 241 initializes all the bots for theexternal database, processing advances to a block 242. In block 242, thebots extract and convert data in accordance with their preprogrammedinstructions. As each bot extracts and converts data from the externaldatabase (25), processing advances to a software block 209 before thebot completes data storage. The software in block 209 checks theexternal database metadata to see if all fields have been extracted. Ifthe software in block 209 finds no unmapped data fields, then theextracted, converted data is stored in the external database table(146). Alternatively, if there are fields that haven't been extracted,then processing advances to a block 210. The software in block 210prompts the user (20) via the metadata and conversion rules window (702)to provide metadata and conversion rules for each new field. Theinformation regarding the new metadata and conversion rules is stored inthe metadata mapping table (141) and conversion rules table (142) whilethe extracted, converterd data is stored in the external database table(146). It is worth noting at this point that the activation andoperation of bots that don't have unmapped fields continues. Only botswith unmapped fields “wait” for user input before completing datastorage. The new metadata and conversion rule information will be usedthe next time bots are initialized in accordance with the frequencyestablished by the user (20). In either event, system processing thenpasses, on to software block 244.

[0105] The software in block 244 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 248. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 245.

[0106] The software in block 245 checks the bot date table (149) anddeactivates any advanced financial system data bots with creation datesbefore the current system date and retrieves information from the systemsetting table (140), metadata mapping table (141) and conversion rulestable (142). The software in block 245 then initializes data bots foreach field in the metadata mapping table (141) that mapped to theadvanced financial system database (30) in accordance with the frequencyspecified by user (20) in the system settings table (140). Each data botinitialized by software block 245 will store its data in the advancedfinancial system database table (147).

[0107] After the software in block 245 initializes all the bots for theadvanced financial system database, processing advances to a block 246.In block 246, the bots extract and convert data in accordance with theirpreprogrammed instructions in accordance with the frequency specified byuser (20) in the system settings table (140). As each bot extracts andconverts data from the advanced financial system database (30),processing advances to a software block 209 before the bot completesdata storage. The software in block 209 checks the advanced financialsystem database metadata to see if all fields have been extracted. Ifthe software in block 209 finds no unmapped data fields, then theextracted, converted data is stored in the advanced financial systemdatabase table (147). Alternatively, if there are fields that haven'tbeen extracted, then processing advances to a block 210. The software inblock 210 prompts the user (20) via the metadata and conversion ruleswindow (702) to provide metadata and conversion rules for each newfield. The information regarding the new metadata and conversion rulesis stored in the metadata mapping table (141) and conversion rules table(142) while the extracted, converted data is stored in the advancedfinancial system database table (147). It is worth noting at this pointthat the activation and operation of bots that don't have unmappedfields continues. Only bots with unmapped fields “wait” for user inputbefore completing data storage. The new metadata and conversion ruleinformation will be used the next time bots are initialized inaccordance with the frequency established by the user (20). In eitherevent, system processing then passes, on to software block 248.

[0108] The software in block 248 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 264. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 261.

[0109] The software in block 261 checks the bot date table (149) anddeactivates any soft asset management system data bots with creationdates before the current system date and retrieves information from thesystem setting table (140), metadata mapping table (141) and conversionrules table (142). The software in block 261 then initializes data botsfor each field in the metadata mapping table (141) that mapped to a softasset management system database (35) in accordance with the frequencyspecified by user (20) in the system settings table (140). Extractingdata from each soft asset management system ensures that the managementof each soft asset is considered and prioritized within the overallfinancial models for the organization and each enterprise in theorganization. Each data bot initialized by software block 261 will storeits data in the soft asset system table (148).

[0110] After the software in block 261 initializes bots for all softasset management system databases, processing advances to a block 262.In block 262, the bots extract and convert data in accordance with theirpreprogrammed instructions in accordance with the frequency specified byuser (20) in the system settings table (140). As each bot extracts andconverts data from the soft asset management system databases (35),processing advances to a software block 209 before the bot completesdata storage. The software in block 209 checks the metadata for the softasset management system databases to see if all fields have beenextracted. If the software in block 209 finds no unmapped data fields,then the extracted, converted data is stored in the soft asset systemtable (148). Alternatively, if there are fields that haven't beenextracted, then processing advances to a block 210. The software inblock 210 prompts the user (20) via the metadata and conversion ruleswindow (702) to provide metadata and conversion rules for each newfield. The information regarding the new metadata and conversion rulesis stored in the metadata mapping table (141) and conversion rules table(142) while the extracted, converterd data is stored in the soft assetsystem table (148). It is worth noting at this point that the activationand operation of bots that don't have unmapped fields continues. Onlybots with unmapped fields “wait” for user input before completing datastorage. The new metadata and conversion rule information will be usedthe next time bots are initialized in accordance with the frequencyestablished by the user (20). In either event, system processing thenpasses, on to software block 264.

[0111] The software in block 264 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 276. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 265.

[0112] The software in block 265 prompts the user (20) via theidentification and classification rules window (703) to identifykeywords such as company names, brands, trademarks, competitors forpre-specified fields in the metadata mapping table (141). The user (20)also has the option of mapping keywords to other fields in the metadatamapping table (141). After specifying the keywords, the user (20) isprompted to select and classify descriptive terms for each keyword. Theinput from the user (20) is stored in the keyword table (150) in theapplication database before processing advances to a software block 266.

[0113] The software in block 266 checks the bot date table (149) anddeactivates any internet text bots with creation dates before thecurrent system date and retrieves information from the system settingstable (140), the metadata mapping table (141) and the keyword table(150). The software in block 266 then initializes internet text bots foreach field in the metadata mapping table (141) that mapped to a keywordin accordance with the frequency specified by user (20) in the systemsettings table (140) before advancing processing to a software block267.

[0114] Bots are independent components of the application that havespecific tasks to perform. In the case of text bots, their tasks are tolocate, count and classify keyword matches from a specified source andthen store their findings in a specified location. Each text botinitialized by software block 266 will store the location, count andclassification data it discovers in the classified text table (151).Multimedia data can be processed using bots with essentially the samespecifications if software to translate and parse the multimedia contentis included in each bot. Every internet text bot contains theinformation shown in Table 18. TABLE 18 1. Unique ID number (based ondate, hour, minute, second of creation) 2. Creation date (day, hour,minute, second) 3. Storage location 4. Mapping information 5. Home URL6. Keyword 7. Descriptive term 1 To 7 + n. Descriptive term n

[0115] In block 267 the text bots locate and classify data from theexternal database (25) in accordance with their programmed instructionsin accordance with the frequency specified by user (20) in the systemsettings table (140). As each text bot locates and classifies data fromthe internet (40) processing advances to a software block 268 before thebot completes data storage. The software in block 268 checks to see ifall keyword hits are associated with descriptive terms that have beenbeen classified. If the software in block 268 doesn't find anyunclassified “hits”, then the address, count and classified text arestored in the classified text table (151). Alternatively, if there areterms that haven't been classified, then processing advances to a block269. The software in block 269 prompts the user (20) via theidentification and classification rules window (703) to provideclassification rules for each new term. The information regarding thenew classification rules is stored in the keyword table (150) while thenewly classified text is stored in the classified text table (151). Itis worth noting at this point that the activation and operation of botsthat don't have unclassified fields continues. Only bots withunclassified fields will “wait” for user input before completing datastorage. The new classification rules will be used the next time botsare initialized in accordance with the frequency established by the user(20). In either event, system processing then passes, on to a softwareblock 270.

[0116] The software in block 270 checks the bot date table (149) anddeactivates any external database text bots with creation dates beforethe current system date and retrieves information from the systemsettings table (140), the metadata mapping table (141) and the keywordtable (150). The software in block 270 then initializes externaldatabase text bots for each field in the metadata mapping table (141)that mapped to a keyword in accordance with the frequency specified byuser (20) in the system settings table (140) before advancing processingto a software block 271. Every text bot initialized by software block270 will store the location, count and classification data it discoversin the classified text table (151). Every external database text botcontains the information shown in Table 19. TABLE 19 1. Unique ID number(based on date, hour, minute, second of creation) 2. Creation date (day,hour, minute, second) 3. Storage location 4. Mapping information 5. DataSource 6. Keyword 7. Descriptive term 1 To 7 + n. Descriptive term n

[0117] In block 271 the text bots locate and classify data from theexternal database (25) in accordance with its programmed instructionswith the frequency specified by user (20) in the system settings table(140). As each text bot locates and classifies data from the externaldatabase (25) processing advances to a software block 268 before the botcompletes data storage. The software in block 268 checks to see if allkeyword hits are associated with descriptive terms that have been beenclassified. If the software in block 268 doesn't find any unclassified“hits”, then the address, count and classified text are stored in theclassified text table (151). Alternatively, if there are terms thathaven't been classified, then processing advances to a block 269. Thesoftware in block 269 prompts the user (20) via the identification andclassification rules window (703) to provide classification rules foreach new term. The information regarding the new classification rules isstored in the keyword table (150) while the newly classified text isstored in the classified text table (151). It is worth noting at thispoint that the activation and operation of bots that don't haveunclassified fields continues. Only bots with unclassified fields “wait”for user input before completing data storage. The new classificationrules will be used the next time bots are initialized in accordance withthe frequency established by the user (20). In either event, systemprocessing then passes, on to software block 276.

[0118] The software in block 276 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change then processing advances to asoftware block 280. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 277.

[0119] The software in block 277 checks the system setting table (140)to see if there is geocoded data in the application database (50) and todetermine which on-line geocoding service (Centrus™ from QM Soft orMapMarker™ from Mapinfo) is being used. If geospatial data is not beingused, then processing advances to a block 291. Alternatively, if thesoftware in block 277 determines that geospatial data is being used,processing advances to a software block 278.

[0120] The software in block 278 prompts the user (20) via thegeospatial meaure definitions window (709) to define the measures thatwill be used in evaluating the elements of value. After specifying themeasures, the user (20) is prompted to select the geospatial locus foreach measure from the data already stored in the application database(50). The input from the user (20) is stored in the geospatial measurestable (152) in the application database before processing advances to asoftware block 279.

[0121] The software in block 279 checks the bot date table (149) anddeactivates any geospatial bots with creation dates before the currentsystem date and retrieves information from the system settings table(140), the metadata mapping table (141) and the geospatial measurestable (152). The software in block 279 then initializes geospatial botsfor each field in the metadata mapping table (141) that mapped togeospatial data in the application database (50) in accordance with thefrequency specified by user (20) in the system settings table (140)before advancing processing to a software block 280.

[0122] Bots are independent components of the application that havespecific tasks to perform. In the case of geospatial bots, their tasksare to calculate user specified measures using a specified geocodingservice and then store the measures in a specified location. Eachgeospatial bot initialized by software block 279 will store the measuresit calculates in the application database table where the geospatialdata was found. Tables that could include geospatial data include: thebasic financial system table (143), the operation system table (144),the human resource system table (145), the external database table(146), the advanced finance system table (147) and the soft asset systemtable (148). Every geospatial bot contains the information shown inTable 20. TABLE 20 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (day, hour, minute, second) 3.Mapping information 4. Storage location 5. Geospatial locus 6.Geospatial measure 7. Geocoding service

[0123] In block 280 the geospatial bots locate data and completemeasurements in accordance with their programmed instructions with thefrequency specified by the user (20) in the system settings table (140).As each geospatial bot retrieves data and calculates the geospatialmeasures that have been specified, processing advances to a block 281before the bot completes data storage. The software in block 281 checksto see if all geospatial data located by the bot has been been measured.If the software in block 281 doesn't find any unmeasured data, then themeasurement is stored in the application database (50). Alternatively,if there are data elements that haven't been measured, then processingadvances to a block 282. The software in block 282 prompts the user (20)via the geospatial measure definition window (709) to providemeasurement rules for each new term. The information regarding the newmeasurement rules is stored in the geospatial measures table (152) whilethe newly calculated measurement is stored in the appropriate table inthe application database (50). It is worth noting at this point that theactivation and operation of bots that don't have unmeasured fieldscontinues. Only the bots with unmeasured fields “wait” for user inputbefore completing data storage. The new measurement rules will be usedthe next time bots are initialized in accordance with the frequencyestablished by the user (20). In either event, system processing thenpasses on to a software block 291.

[0124] The software in block 291 checks: the basic financial systemtable (143), the operation system table (144), the human r source systemtable (145), the external database table (146), the advanced financesystem table (147), the soft asset system table (148), the classifiedtext table (151) and the geospatial measures table (152) to see if datais missing from any of the periods required for system calculation. Therange of required dates was previously calculated by the software inblock 202. If there is no data missing from any period, then processingadvances to a software block 293. Alternatively, if there is missingdata for any field for any period, then processing advances to a block292.

[0125] The software in block 292, prompts the user (20) via the missingdata window (704) to specify the method to be used for filling theblanks for each item that is missing data. Options the user (20) canchoose from for filling the blanks include: the average value for theitem over the entire time period, the average value for the item over aspecified period, zero, the average of the preceeding item and thefollowing item values and direct user input for each missing item. Ifthe user (20) doesn't provide input within a specified interval, thenthe default missing data procedure specified in the system settingstable (140) is used. When all the blanks have been filled and stored forall of the missing data, system processing advances to a block 293.

[0126] The software in block 293 calculates attributes by item for eachnumeric data field in the basic financial system table (143), theoperation system table (144), the human resource system table (145), theexternal database table (146), the advanced finance system table (147)and the soft asset system table (148). The attributes calculated in thisstep include: cumulative total value, the period to period rate ofchange in value, the rolling average value and a series of time laggedvalues. In a similar fashion the software in block 293 calculatesattributes for each date field in the specified tables including timesince last occurrence, cumulative time since first occurrence, averagefrequency of occurrence and the rolling average frequency of occurrence.The numbers derived from numeric and date fields are collectivelyreferred to as “item performance indicators”. The software in block 293also calculates pre-specified combinations of variables called compositevariables for measuring the strength of the different elements of value.The item performance indicators are stored in the table where the itemsource data was obtained and the composite variables are stored in thecomposite variables table (153) before processing advances to a block294.

[0127] The software in block 294 uses attribute derivation algorithmssuch as the AQ program to create combinations of the variables thatweren't pre-specified for combination. While the AQ program is used inthe preferred embodiment of the present invention, other attributederivation algorithms such as the LINUS algorithms, may be used to thesame effect. The software creates these attributes using both itemvariables that were specified as “element” variables and item variablesthat were not. The resulting composite variables are stored in thecomposite variables table (153) before processing advances to a block295.

[0128] The software in block 295 uses Data Envelopement Analysis(hereinafter, DEA) to determine the relative industry ranking of theorganization and enterprises being examined using the compositevariables calculated in block 293. For example, DEA can be used todetermine the relative efficiency of a company in receiving favorablepress mentions per dollar spent on advertising. When all pre-specifiedindustry rankings have been calculated and stored in the industryranking table (154), processing advances to a software block 296.

[0129] The software in block 296 uses pattern-matching algorithms toassign pre-designated data fields for different elements of value topre-defined groups with numerical values. This type of analysis isuseful in classifying purchasing patterns and/or communications patternsas “heavy”, “light”, “moderate” or “sporadic”. The assignments arecalculated using the “rolling average” value for each field. Theclassification and the numeric value associated with the classificationare stored in the application database (50) table where the data fieldis located before processing advances to a block 297.

[0130] The software in block 297 retrieves data from the metadatamapping table (141), creates and then stores the definitions for thepre-defined components of value in the components of value definitiontable (155). As discussed previously, the revenue component of value isnot divided into sub-components, the expense value is divided into fivesub-components (the cost of raw materials, the cost of manufacture ordelivery of service, the cost of selling, the cost of support and thecost of administration) and the capital value is divided into sixsub-components: (cash, non-cash financial assets, production equipment,other assets, financial liabilities and equity) in the preferredembodiment. When data storage is complete, processing advances to asoftware block 302 to begin the analysis of the extracted data usinganalysis bots.

Analysis Bots

[0131] The flow diagrams in FIG. 6A, FIG. 6B and FIG. 6C detail theprocessing that is completed by the portion of the application software(300) that programs analysis bots to:

[0132] 1. identify the item variables, item performance indicators andcomposite variables for each enterprise, element of value andsub-element of value that drive the components of value (revenue,expense and changes in capital),

[0133] 2. create vectors that summarize the performance of the itemvariables and item performance indicators for each enterprisecontribution, element of value and sub-element of value,

[0134] 3. determine the appopriate cost of capital and value theorganization and enteprise real options;

[0135] 4. determine the appopriate cost of capital, value and allocatethe industry real options to each organization or enterprise on thebasis of relative element strength;

[0136] 5. determine the expected life of each element of value andsub-element of value;

[0137] 6. calculate the organization and enterprise current operationvalues and value the revenue, expense and capital components saidcurrent operations using the information prepared in the previous stageof processing;

[0138] 7. specify and optimize predictive models to determine therelationship between the vectors determined in step 2 and the revenue,expense and capital values determined in step 6,

[0139] 8. combine the results of the fifth, sixth and seventh stages ofprocessing to determine the value of each, enterprise contribution,element and sub-element (as shown in Table 7);

[0140] Processing in this portion of the application begins in softwareblock 302. The software in block 302 checks the system settings table(140) in the application database (50) to determine if the currentcalculation is a new calculation or a structure change. If thecalculation is not a new calculation or a structure change thenprocessing advances to a software block 3110. Alternatively, if thecalculation is new or a structure change, then processing advances to asoftware block 303.

[0141] The software in block 303 retrieves data from the meta datamapping table (141) and the soft asset system table (148) and thenassigns item variables, item performance indicators and compositevariables to each element of value using a two step process. First, itemvariables and item performance indicators are assigned to elements ofvalue based on the soft asset management system they correspond to (forexample, all item variables from a brand management system and all itemperformance indicators derived from brand management system variablesare assigned to the brand element of value). Second, pre-definedcomposite variables are assigned to the element of value they wereassigned to measure in the metadata mapping table (141). After theassignment of variables and indicators to elements is complete, theresulting assignments are saved to the element of value definition table(155) and processing advances to a block 304.

[0142] The software in block 304 checks the bot date table (149) anddeactivates any clustering bots with creation dates before the currentsystem date. The software in block 304 then initializes bots as requiredfor each component of value. The bots: activate in accordance with thefrequency specified by the user (20) in the system settings table (140),retrieve the information from the system settings table (140), themetadata mapping table (141) and the component of value definition table(156) as required and define segments for the component of value databefore saving the resulting cluster information in the applicationdatabase (50).

[0143] Bots are independent components of the application that havespecific tasks to perform. In the case of predictive model bots, theirprimary task is to segment the component and sub-component of valuevariables into distinct clusters that share similar characteristics. Theclustering bot assigns a unique id number to each “cluster” itidentifies and stores the unique id numbers in the cluster id table(157). Every item variable for every component and sub-component ofvalue is assigned to one of the unique clusters. The cluster id for eachvariable is saved in the data record for each item variable in the tablewhere it resides. The item variables are segmented into a number ofclusters less than or equal to the maximum specified by the user (20) inthe system settings. The data is segmented using the “default”clustering algorithm the user (20) specified in the system settings. Thesystem of the present invention provides the user (20) with the choiceof several clustering algorithms including: an unsupervised “Kohonen”neural network, K-nearest neighbor, Expectation Maximization (EM) andthe segmental K-means algorithm. For algorithms that normally requirethe number of clusters to be specified the bot will iterate the numberof clusters until it finds the cleanest segmentation for the data. Everyclustering bot contains the information shown in Table 21. TABLE 21 1.Unique ID number (based on date, hour, minute, second of creation) 2.Creation date (day, hour, minute, second) 3. Mapping information 4.Storage location 5. Component or subcomponent of value 6. Clusteringalgorithm type 7. Maximum number of clusters 8. Variable 1 . . . 8 + n.Variable n

[0144] When bots in block 304 have identified and stored clusterassignments for the item variables associated with each component andsubcomponent of value, processing advances to a software block 305.

[0145] The software in block 305 checks the bot date table (149) anddeactivates any predictive model bots with creation dates before thecurrent system date. The software in block 305 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the element of value definition table (155) and thecomponent of value definition table (156) required to initializepredictive model bots for each component of value at every level in theorganization.

[0146] Bots are independent components of the application that havespecific tasks to perform. In the case of predictive model bots, theirprimary task is determine the relationship between the item variables,item performance indicators and composite variables (collectivelyhereinafter, “the variables”) and the components of value (andsub-components of value) by cluster at each level of the organization. Aseries of predictive model bots are initialized at this stage because itis impossible to know in advance which predictive model type willproduce the “best” predictive model for the data from each commercialenterprise. The series for each model includes 9 predictive model bottypes: neural network; CART; projection pursuit regression; generalizedadditive model (GAM), redundant regression network; boosted Naive BayesRegression; MARS; linear regression; and stepwise regression. Thesoftware in block 305 generates this series of predictive model bots forthe levels of the organization shown in Table 22. TABLE 22 Predictivemodels by organization level Organization: Enterprise variablesrelationship to organization revenue component of value by clusterEnterprise variables relationship to organization expense subcomponentsof value by cluster Enterprise variables relationship to organizationcapital change subcomponents of value by cluster Enterprise: Elementvariables relationship to enterprise revenue component of value bycluster Element variables relationship to enterprise expensesubcomponents of value by cluster Element variables relationship toenterprise capital change subcomponents of value by cluster Element ofValue: Sub-element of value variables relationship to element of value

[0147] Every predictive model bot contains the information shown inTable 23. TABLE 23 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (day, hour, minute, second) 3.Mapping information 4. Storage location 5. Component or subcomponent ofvalue 6. Cluster (ID) 7. Enterprise, Element or Sub-Element ID 8.Predictive Model Type 9. Variable 1 . . . 9 + n. Variable n

[0148] After predictive model bots for each level in the organizationare initialized, the bots activate in accordance with the frequencyspecified by the user (20) in the system settings table (140). Onceactivated, the bots retrieve the required data from the appropriatetable in the application database (50) and randomly partition the itemvariables, item performance indicators and composite variables into atraining sets and a test set. The software in block 305 uses“bootstrapping” where the different training data sets are created byre-sampling with replacement from the original training set, so datarecords may occur more than once. The same sets of data will be used totrain and then test each predictive model bot. When the predictive modelbots complete their training and testing, processing advances to a block306.

[0149] The software in block 306 uses a variable selection algorithmsuch as stepwise regression (other algorithms can be used) to combinethe results from the predictive model bot analyses for each model todetermine the best set of variables for each model. The models havingthe smallest amount of error as measured by applying the mean squarederror algorithm to the test data are given preference in determining thebest set of variables. As a result of this processing the best set ofvariables contain the item variables, item performance indicators andcomposite variables that correlate most strongly with changes in thecomponents of value. The best set of variables will hereinafter bereferred to as the “value drivers”. Eliminating low correlation factorsfrom the initial configuration of the vector creation algorithmsincreases the efficiency of the next stage of system processing. Othererror algorithms alone or in combination may be substituted for the meansquared error algorithm. After the best set of variables have beenselected and stored in the element variables table (158) for all modelsat all levels, the software in block 306 tests the independence of thevalue drivers at the enterprise, element and sub-element level beforeprocessing advances to a block 307.

[0150] The software in block 307 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation, a structure change or if the interaction betweenvalue drivers has changed from being highly correlated to beingindependent. If the calculation is not a new calculation, a structurechange or a change to independent value driver status, then processingadvances to a software block 310. Alternatively, if the calculation isnew, a structure change or a change to independent status, thenprocessing advances to a software block 308. The software in block 308checks the bot date table (149) and deactivates any induction bots withcreation dates before the current system date. The software in block 308then retrieves the information from the system settings table (140), themetadata mapping table (141), the component of value definition table(156) and the element variables table (158) as required to initializeinduction model bots for each enterprise, element of value andsub-element of value at every level in the organization in accordancewith the frequency specified by the user (20) in the system settingstable (140) before processing advances to a block 309.

[0151] Bots are independent components of the application that havespecific tasks to perform. In the case of induction bots, their primarytasks are to refine the item variable, item performance indicator andcomposite variable selection to reflect only causal variables and toproduce formulas, (hereinafter, vectors) that summarize the relationshipbetween the item variables, item performance indicators and compositevariables and changes in the component or sub-component of value beingexamined. (Note: these variables are simply grouped together torepresent an element vector when they are dependent). A series ofinduction bots are initialized at this stage because it is impossible toknow in advance which induction algorithm will produce the “best” vectorfor the best fit variables from each model. The series for each modelincludes 4 induction bot types: entropy minimization, LaGrange, Bayesianand path analysis. The software in block 308 generates this series ofinduction bots for each set of variables stored in the element variablestable (158) in the previous stage in processing. Every induction botcontains the information shown in Table 24. TABLE 24 1. Unique ID number(based on date, hour, minute, second of creation) 2. Creation date (day,hour, minute, second) 3. Mapping information 4. Storage location 5.Component or subcomponent of value 6. Cluster (ID) 7. Enterprise,Element or Sub-Element ID 8. Variable Set 9. Induction algorithm type

[0152] After the induction bots are initialized by the software in block308 processing passes to a sotware block 309. In block 309 bots activatein accordance with the frequency specified by the user (20) in thesystem settings table (140). Once activated, they retrieve the elementvariable information for each model from the element variable table(158) and sub-divides the variables into two sets, one for training andone for testing. The same set of training data is used by each of thedifferent types of bots for each model. After the induction botscomplete their processing for each model, the software in block 309 usesa model selection algorithm to identify the vector that best fits thedata for each enterprise, element or sub-element being analyzed. For thesystem of the present invention, a cross validation algorithm is usedfor model selection. The software in block 309 saves the the best fitvector in the vector table (159) in the application database (50) andprocessing returns to advances to a block 310. The software in block 310tests the value drivers or vectors to see if there are “missing” valuedrivers that are influencing the results. If the software in block 310doesn't detect any missing value drivers, then system processingadvances to a block 322. Alternatively, if missing value drivers aredetected by the software in block 310, then processing advances to asoftware block 321.

[0153] The software in block 321 prompts the user (20) via the variableidentification window (710) to adjust the specification(s) for theaffected enterprise, element of value or subelement of value. After theinput from the user (20) is saved in the system settings table (140)and/or the element of value definition table (155), system processingadvances to a software block 323. The software in block 323 checks thein the system settings table (140) and/or the element of valuedefinition table (155) to see if there any changes in structure. Ifthere have been changes in the structure, then processing advances to ablock 205 and the system processing described previously is repeated.Alternatively, if there are no changes in structure, then processingadvances to a block 325.

[0154] The software in block 325 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change, then processing advances to asoftware block 329. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 326.

[0155] The software in block 326 checks the bot date table (149) anddeactivates any option bots with creation dates before the currentsystem date. The software in block 326 then retrieves the informationfrom the system settings table (140), the metadata mapping table (141),the basic financial system database (143), the external database table(146) and the advanced finance system table (147) as required toinitialize option bots for the organization, the industry and eachenterprise in the organization before processing advances to a block327.

[0156] Bots are independent components of the application that havespecific tasks to perform. In the case of option bots, their primarytasks are to calculate the cost of capital (if the user (20) hasn'tspecified the cost of capital in the system settings table (140)) andvalue the real options for the industry, the organization, and eachenterprise in the organization. The base cost of capital is calculatedusing a well known formula for the industry and each enterprise. Thebots then use the data regarding the similarity of the “soft” assetprofiles between the proposed real option activity and the existingindustry, organization and enterprise profiles to determine the multipleon the cost of capital that will be used in valuing the real option. Thecloser the real option profile is to the existing profile, the closerthe multiple is to one. If sufficient data is available, patternmatching algorithms can be used to replace the assessment by the user(20). After the cost of capital multiple has been determined, the valueof the real option is calculated using dynamic programming algorithms ina manner that is well known and stored in the real option value table(162). Real option values are calculated using dynamic programmingalgorithms. The real option can be valued using other algorithmsincluding binomial, neural network or Black Scholes algorithms. Thesoftware in block 326 generates option bots for the industry, theorganization and each enterprise in the organization.

[0157] Option bots contain the information shown in Table 25. TABLE25 1. Unique ID number (based on date, hour, minute, second of creation)2. Creation date (day, hour, minute, second) 3. Mapping information 4.Storage location 5. Organization or Enterprise ID 6. Real Option Type(Industry, Organization or Enterprise) 7. Real Option 8. Allocation %(if applicable)

[0158] After the option bots are initialized by the software in block326 processing passes to a block 327. In block 327 the bots activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). After being activated, the bots retrieveinformation for the organization, the industry and each enterprise inthe organization from the basic financial system database (143), theexternal database table (146) and the advanced finance system table(147) as required to complete the option valuation. After the cost ofcapital multiple has been determined the value of the real option iscalculated using dynamic programming algorithms in a manner that is wellknown. The resulting values are then saved in the real option valuetable (162) in the application database (50) before processing advancesto a block 328.

[0159] The software in block 328 uses the item performance indicatorsproduced by DEA analysis in blocks 304, 308 and 314 and the percentageof industry real options controlled by the enterprise to determine theallocation percentage for industry options. The more dominant theorganization and enterprise—as indicated by the industry rank for theintangible element indicators, the greater the allocation of industryreal options. After the software in block 328 saves the informationregarding the allocation of industry real options to the organizationand each enterprise in the organization to the real option value table(162) in the application database (50) before advancing processing to ablock 329.

[0160] The software in block 329 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change, then processing advances to asoftware block 333. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 330.

[0161] The software in block 330 checks the bot date table (149) anddeactivates any cash flow bots with creation dates before the currentsystem date. The software in block 326 then retrieves the informationfrom the system settings table (140), the metadata mapping table (141)and the component of value definition table (156) as required toinitialize cash flow bots for the organization and each enterprise inthe organization in accordance with the frequency specified by the user(20) in the system settings table (140) before processing advances to ablock 331.

[0162] Bots are independent components of the application that havespecific tasks to perform. In the case of cash flow bots, their primarytasks are to calculate the cash flow for the organization and eachenterprise in the organization for every time period where data isavailable and to forecast a steady state cash flow for the organizationand each enterprise in the organization. Cash flow is calculated using awell known formula where cash flow equals period revenue minus periodexpense plus the period change in capital plus non-cashdepreciation/amortization for the period. The steady state cash flow iscalculated for the organization and each enterprise in the organizationusing forecasting methods identical to those disclosed previously inU.S. Pat. No. 5,615,109 to forecast revenue, expenses, capital changesand depreciation seperately before calculating the cash flow. Thesoftware in block 326 generates cash flow bots for the organization andeach enterprise in the organization.

[0163] Every cash flow bot contains the information shown in Table 26.TABLE 26 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (day, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization or Enterprise ID 6.Components of value

[0164] After the cash flow bots are initialized by the software in block330 processing passes to a block 331. In block 331 the bots activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). After being activated the bots retrieve thecomponent of value information for the organization and each enterprisein the organization from the component of value definition table (156).The cash flow bots then complete the calculation and forecast of cashflow for the organization and each enterprise in the organization beforesaving the resulting values by period in the cash flow table (161) inthe application database (50) before processing advances to a block 333.

[0165] The software in block 333 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change, then processing advances to asoftware block 343. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 341.

[0166] The software in block 341 checks the bot date table (149) anddeactivates any element life bots with creation dates before the currentsystem date. The software in block 341 then retrieves the informationfrom the system settings table (140), the metadata mapping table (141)and the element of value definition table (155) as required toinitialize element life bots for each element and sub-element of valuein the organization before processing advances to a block 342.

[0167] Bots are independent components of the application that havespecific tasks to perform. In the case of element life bots, theirprimary task is to determine the expected life of each element andsub-element of value for each enterprise in the organization. There arethree methods for evaluating the expected life of the elements andsub-elements of value. Elements of value that are defined by apopulation of members (such as: channel partners, customers, employeesand vendors) will have their lives estimated by analyzing andforecasting the lives of the members of the population. The forecastingof member lives will be determined by the “best” fit solution fromcompeting life estimation methods including the Iowa type survivorcurves, Weibull distribution survivor curves, Gompertz-Makeham survivorcurves, polynomial equations and the forecasting methodology disclosedin U.S. Pat. No. 5,615,109. Elements of value (such as some parts ofIntellectual Property—patents) that have legally defined lives will havetheir lives calculated using the time period between the current dateand the expiration date of the element or sub-element. Finally, elementsof value and sub-element of value (such as brand names, informationtechnology and processes) that do not have defined lives and that do notconsist of a collection of members will have their lives estimated bycomparing the relative strength and stability of the element vectorswith the relative stability of the enterprise CAP. The resulting valuesare stored in the element of value definition table (155) for eachelement and sub-element of value of each enterprise in the organization.

[0168] Every element life bot contains the information shown in Table27. TABLE 27 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (day, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Element of Sub-Element of Value 6.Life Estimation Method (population analysis, date calculation orrelative CAP)

[0169] After the element life bots are initialized by the software inblock 341 processing passes to block 342. In block 342 the element lifebots activate in accordance with the frequency specified by the user(20) in the system settings table (140). After being activated, the botsretrieve information for each element and sub-element of value from theelement of value definition table (155) as required to complete theestimate of element life. The resulting values are then saved in theelement of value definition table (155) in the application database (50)before processing advances to a block 343.

[0170] The software in block 343 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change, then processing advances to asoftware block 402. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 345.

[0171] The software in block 345 checks the bot date table (149) anddeactivates any component capitalization bots with creation dates beforethe current system date. The software in block 341 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141) and the component of value definition table (156) asrequired to initialize component capitalization bots for theorganization and each enteprise in the organization before processingadvances to a block 346.

[0172] Bots are independent components of the application that havespecific tasks to perform. In the case of component capitalization bots,their task is to determine the capitalized value of the components ofvalue, forecast revenue, expense or capital requirements, for theorganization and for each enterprise in the organization in accordancewith the formula shown in Table 28. TABLE 28 Value = F_(f1)/(1 + K) +F_(f2)/(1 + K)² + F_(f3)/(1 + K)³ + F_(f4)/(1 + K)⁴ + (F_(f4) × (1 +g))/(1 + K)⁵) + (F_(f4) × (1 + g)²)/(1 + K)⁶) . . . + (F_(f4) × (1 +g)^(N))/(1 + K)^(N+4)) F_(fx) = Forecast revenue, expense or capitalrequirements for year x after valuation date (from advanced financesystem) N = Number of years in CAP (from prior calculation) K = Cost ofcapital - % per year (from prior calculation) g = Forecast growth rateduring CAP - % per year (from advanced finance system)

[0173] After the capitalized value of every component and sub-componentof value is complete, the results are stored in the component of valuedefinition table (156) in the application database (50).

[0174] Every component capitalization bot contains the information shownin Table 29. TABLE 29 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (day, hour, minute, second) 3.Mapping information 4. Storage location 5. Organization or Enterprise ID6. Component of Value (Revenue, Expense or Capital Change) 7. SubComponent of Value

[0175] After the component capitalization bots are initialized by thesoftware in block 345 processing passes to block 346. In block 346 thecomponent capitalization bots activate in accordance with the frequencyspecified by the user (20) in the system settings table (140). Afterbeing activated, the bots retrieve information for each component andsub-component of value from the advanced finance system table (147) andthe component of value definition table (156) as required to calculatethe capitalized value of each component. The resulting values are thensaved in the component of value definition table (156) in theapplication database (50) before processing advances to a block 347.

[0176] The software in block 347 checks the bot date table (149) anddeactivates any valuation bots with creation dates before the currentsystem date. The software in block 347 then retrieves the informationfrom the system settings table (140), the metadata mapping table (141),the element of value definition table (155), the component of valuedefinition table (156) as required to initialize valuation bots for eachenterprise, element and sub-element of value in the organization beforeprocessing advances to a block 348.

[0177] Bots are independent components of the application that havespecific tasks to perform. In the case of valuation bots, their task isto calculate the contribution of every enterprise, element of value andsub-element of value in the organization using the overall procedureoutlined in Table 7. The first step in completing the calculation inaccordance with the procedure outlined in Table 7, is determining therelative contribution of each enterprise and element of value by using aseries of predictive models to find the best fit relationship between:

[0178] 1. the enterprise contribution vectors and the organizationcomponents of value;

[0179] 2. the element of value vectors and the enterprise components ofvalue; and

[0180] 3. the sub-element of value vectors and the element of value theycorrespond to.

[0181] The system of the present invention uses 9 different types ofpredictive models to determine relative contribution: neural network;CART; projection pursuit regression; generalized additive model (GAM),redundant regression network; boosted Naïve Bayes Regression; MARS;linear regression; and stepwise regression to determine relativecontribution. The model having the smallest amount of error as measuredby applying the mean squared error algorithm to the test data is thebest fit model. The “relative contribution algorithm” used forcompleting the analysis varies with the model that was selected as the“best-fit”. For example, if the “best-fit” model is a neural net model,then the portion of revenue attributable to each input vector isdetermined by the formula shown in Table 30. TABLE 30$\left( {\sum\limits_{k = 1}^{k = m}{\sum\limits_{j = 1}^{j = n}{I_{jk} \times {O_{k}/{\sum\limits_{j = 1}^{j = n}I_{ik}}}}}} \right)/{\sum\limits_{k = 1}^{k = m}{\sum\limits_{j = 1}^{j = m}{I_{jk} \times O_{k}}}}$

[0182] After the relative contribution of each enterprise, element ofvalue and sub-element of value is determined, the results of thisanalysis are combined with the previously calculated informationregarding element life and -capitalized component value to complete thevaluation of each: enterprise contribution, element of value andsub-element using the approach shown in Table 31. TABLE 31 Percent-Element Gross Value age Lif/CAP Net Value Revenue value = $120 M 20% 80%Value = $19.2 M Expense value = ($80 M) 10% 100%  Value = ($8.0) MCapital value = ($5 M)  5% 80% Value = ($0.2) M Total value = $35 M Netvalue for this element: Value = $11.0 M

[0183] The resulting values are stored in the element of valuedefinition table (155) for each element and sub-element of value of eachenterprise in the organization.

[0184] Every valuation bot contains the information shown in Table 32.TABLE 32 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (day, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Enterprise Contribution, Element ofValue or Sub-Element of Value 6. Organization, Enteprise or Element ofValue ID

[0185] After the valuation bots are initialized by the software in block347 processing passes to block 348. In block 348 the valuation botsactivate in accordance with the frequency specified by the user (20) inthe system settings table (140). After being activated, the botsretrieve information from the element of value definition table (155)and the component of value definition table (156) as required tocomplete the valuation. The resulting values are then saved in theelement of value definition table (155) in the application database (50)before processing advances to a block 349.

[0186] The software in block 349 checks the bot date table (149) anddeactivates any residual bots with creation dates before the currentsystem date. The software in block 349 then retrieves the informationfrom the system settings table (140), the metadata mapping table (141)and the element of value definition table (155) as required toinitialize residual bots for each enterprise in the organization.

[0187] Bots are independent components of the application that havespecific tasks to perform. In the case of residual bots, their task isto retrieve data from the as required from the element of valuedefinition table (155) and the component of value definition table (156)and then calculate the residual going concern value for the organizationand each enterprise in the organization in accordance with the formulashown in Table 33. TABLE 33 Residual Going Concern Value = TotalCurrent-Operation Value − Σ Financial Asset Values − Σ Elements of value

[0188] Every residual bot contains the information shown in Table 34.TABLE 34 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (day, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization or Enterprise ID

[0189] After the residual bots are initialized by the software in block348 processing passes to block 349. In block 349 the residual botsactivate in accordance with the frequency specified by the user (20) inthe system settings table (140). After being activated, the botsretrieve information from the element of value definition table (155)and the component of value definition table (156) as required tocomplete the residual calculation for the organization or enterprise.After the calculation is complete, the resulting values are then savedin the element of value definition table (155) in the applicationdatabase (50) before processing advances to a block 402.

ANALYZE MARKET SENTIMENT

[0190] The flow diagram in FIG. 7 details the processing that iscompleted by the portion of the application software (400) that analyzesthe market sentiment for the enterprises in the organization. Processingbegins in a software block 402.

[0191] The software in block 402 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change, then processing advances to asoftware block 409. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 404.

[0192] The software in block 404 checks the bot date table (149) anddeactivates any sentiment calculation bots with creation dates beforethe current system date. The software in block 404 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the external database table (146), the element of valuedefinition table (155), the component of value definition table (156)and the real option value table (162) as required to initializesentiment calculation bots for each enterprise in the organization.

[0193] Bots are independent components of the application that havespecific tasks to perform. In the case of sentiment calculation bots,their task is to retrieve data as required from: the external databasetable (146), the element of value definition table (155), the componentof value definition table (156) and the real option value table (162)then calculate the sentiment for each enterprise in the organization inaccordance with the formula shown in Table 35. TABLE 35 Sentiment =Total Market Value − Total Current-Operation Value − Σ Real OptionValues

[0194] Every sentiment calculation bot contains the information shown inTable 36. TABLE 36 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (day, hour, minute, second) 3.Mapping information 4. Storage location 5. Enterprise ID

[0195] After the sentiment calculation bots are initialized by thesoftware in block 404 processing passes to block 405. In block 405 thesentiment calculation bots activate in accordance with the frequencyspecified by the user (20) in the system settings table (140). Afterbeing activated, the bots retrieve information from the externaldatabase table (146), the element of value definition table (155), thecomponent of value definition table (156) and the real option valuetable (162) as required to complete the sentiment calculation for eachenterprise. After the calculation is complete, the resulting values arethen saved in the enterprise sentiment table (166) in the applicationdatabase (50) before processing advances to a block 409.

[0196] The software in block 409 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change, then processing advances to asoftware block 412. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 410.

[0197] The software in block 410 checks the bot date table (149) anddeactivates any sentiment factor bots with creation dates before thecurrent system date. The software in block 410 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the external database table (146), the element of valuedefinition table (155), the component of value definition table (156)and the real option value table (162) as required to initializesentiment factor bots for each enterprise in the organization.

[0198] Bots are independent components of the application that havespecific tasks to perform. In the case of sentiment factor bots, theirprimary task is to calculate sentiment related attributes includingcumulative total value, the period to period rate of change in value,the rolling average value, a series of time lagged values as well aspre-specified combinations of variables called composite variables. Thebots also use attribute derivation algorithms such as the AQ program tocreate combinations of the variables that weren't pre-specified forcombination. While the AQ program is used in the preferred embodiment ofthe present invention, other attribute derivation algorithms such as theLINUS algorithms, may be used to the same effect. The newly calculatedsentiment factors are stored in the sentiment factor table (169) beforeprocessing advances to a block 411.

[0199] Every sentiment factor bot contains the information shown inTable 37. TABLE 37 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (day, hour, minute, second) 3.Mapping information 4. Storage location 5. Enterprise ID

[0200] After the sentiment factor bots are initialized by the softwarein block 410 processing passes to block 411. In block 411 the sentimentfactor bots activate in accordance with the frequency specified by theuser (20) in the system settings table (140). After being activated, thebots retrieve information from the external database table (146), theelement of value definition table (155), the component of valuedefinition table (156) and the real option value table (162) as requiredto generate the sentiment factors for each enterprise. After thecalculation is complete, the resulting values are then saved in thesentiment factors table (169) in the application database (50) beforeprocessing advances to a block 412.

[0201] The software in block 412 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change, then processing advances to asoftware block 502. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 413.

[0202] The software in block 413 checks the bot date table (149) anddeactivates any sentiment analysis bots with creation dates before thecurrent system date. The software in block 413 then retrieves theinformation from the system settings table (140), the metadata mappingtable (141), the external database table (146), the element of valuedefinition table (155), the component of value definition table (156),the real option value table (162), the enteprise sentiment table (166)and the sentiment factors table (169) as required to initializesentiment analysis bots for each enterprise in the organization.

[0203] Bots are independent components of the application that havespecific tasks to perform. In the case of sentiment analysis bots, theirprimary task is determine the relationship between sentiment factors andthe calculated sentiment for each enterprise in the organization. Aseries of predictive model bots are initialized at this stage because itis impossible to know in advance which predictive model type willproduce the “best” predictive model for the data from each commercialenterprise. The series for each model includes 9 predictive model bottypes: neural network; CART; projection pursuit regression; generalizedadditive model (GAM), redundant regression network; boosted Naive BayesRegression; MARS; linear regression; and stepwise regression.

[0204] Every sentiment analysis bot contains the information shown inTable 38. TABLE 38 1. Unique ID number (based on date, hour, minute,second of creation) 2. Creation date (day, hour, minute, second) 3.Mapping information 4. Storage location 5. Enterprise ID

[0205] After the sentiment analysis bots are initialized by the softwarein block 413 processing passes to block 414. In block 411 the sentimentanalysis bots activate in accordance with the frequency specified by theuser (20) in the system settings table (140). After being activated, thebots retrieve information from the the system settings table (140), themetadata mapping table (141), the enteprise sentiment table (166) andthe sentiment factors table (169) and randomly partition sentimentfactors for each enterprise into a training set and a test set. Thesoftware in block 414 uses “bootstrapping” where the different trainingdata sets are created by re-sampling with replacement from the originaltraining set, so data records may occur more than once. The same sets ofdata will be used to train and then test each predictive model bot. Whenthe predictive model bots complete their training and testing, theresulting sets of “best fit” factors are then saved in the sentimentfactors table (169) in the application database (50) before processingadvances to a block 415.

[0206] The software in block 415 combines the results from the sentimentanalysis from each bot type to determine the best set of sentimentfactors for each enterprise. The models having the smallest amount oferror as measured by applying the mean squared error algorithm to thetest data are given preference in determining the best set of variables.As a result of this processing the best set of variables contain thesentiment factors that correlate most strongly with changes in thecomponents of value. The best set of variables will hereinafter bereferred to as the “sentiment drivers”. The software in block 415 savesan indicator in each item record identifying the sentiment factors thatare “sentiment drivers” before processing advances to block 502.

DISPLAY AND PRINT RESULTS

[0207] The flow diagram in FIG. 8 details the processing that iscompleted by the portion of the application software (500) that createsand displays financial management reports, optionally prints financialmanagement reports and optionally trades company equity securities. Thefinancial management reports use the Value Map® report format tosummarize information about the categories of business value for theorganization and each enterprise in the organization. If there are priorvaluations, then a Value Creation report will be created to highlightchanges in the categories of business value during the period betweenthe prior valuation and the current valuation date.

[0208] System processing in this portion of the application software(900) begins in a block 502. The software in block 502 checks the systemsettings table (140) in the application database (50) to determine ifthe current calculation is a new calculation or a structure change. Ifthe calculation is not a new calculation or a structure change, thenprocessing advances to a software block 505. Alternatively, if thecalculation is new or a structure change, then processing advances to asoftware block 504.

[0209] The software in block 504 checks the bot date table (149) anddeactivates any report bots with creation dates before the currentsystem date. The software in block 504 then retrieves the informationfrom the system settings table (140) and the report table (164) asrequired to determine the format (Value Map® & Value Creation formatand/or traditional: balance sheet, income & cash flow statement format)and type of report (text or graphical) bots that need to be created forthe organization, each enterprise in the organization and thesub-elements of value before processing advances to block 505.

[0210] Bots are independent components of the application that havespecific tasks to perform. In the case of report bots, their primarytasks are to: retrieve data from the system settings table (140), thebasic finance system table (143), the advanced finance system table(147), the element of value definition table (155), the component ofvalue definition table (156) and the real option value table (162),calculate market equity using the formula shown in Table 39 and generatethe reports in the specified formats for the specified time period(s).TABLE 39 Market Equity = (Current Operation Value) + (Σ Real OptionValues) − (Σ Short Term Liabilities) − (Σ Contingent & Long TermLiabilities) − (Book Value of Equity)

[0211] Every report bot contains the information shown in Table 40.TABLE 40 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (day, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Organization, Enterprise or Elementof ValueID 6. Report Format (text or graphical) 7. Report Type (ValueMap ®/Value Creation format or traditional format)

[0212] The general format of the Value Map® Reports is shown in Table 41and Table 42. TABLE 41 Value Map ™ Report XYZ Corporation ASSETS12/31/19XX 12/31/XXXX Current Operation: Financial Assets Cash andMarketable Securities: $7,871,230 $15,097,057 Accounts Receivable$39,881,200 $42,234,410 Inventory $19,801,140 $21,566,540 Property,Plant & Equipment $22,800,000 $21,221,190 Prepaid Expenses $2,071,440$1,795,890 Subtotal Current Operation Assets: $92,425,010 $101,915,087Cash Generating “Soft” Assets Brandnames $17,000,000 $12,000,000Customer Base $62,000,000 $49,500,000 Employees $10,750,000 $8,250,000Strategic Alliances $33,250,000 $33,500,000 Vendors $11,500,000$9,750,000 General Going Concern Value $31,250,000 $31,750,000 SubtotalCash Generating Assets $165,750,000 $144,750,000 Subtotal CurrentOperation $258,175,010 $246,665,087 Real Options: GUI Market Option$12,500,000 $10,000,000 IPX Market Option $17,000,000 $12,500,000Subtotal Enterprise Options $29,500,000 $22,500,000 Industry GrowthOptions: $80,000,000 $60,000,000 Subtotal Real Options $109,500,000$82,500,000 Total Assets & Options $367,675,010 $329,165,087 MarketSentiment $27,123,116 $18,273,698 Total Market Value $394,798,126$347,438,785

[0213] TABLE 42 Value Map ™ Report XYZ Corporation LIABILITIES &SHAREHOLDER EQUITY Liabilities: Accounts Payable $15,895,585 $18,879,949Salaries Payable $8,766,995 $10,468,305 Short Term Debt, Notes$20,189,900 $11,506,130 Payable Taxes Payable $12,430,120 $9,099,880Subtotal Short Term $57,282,600 $49,954,264 Liabilities ContingentLiabilities $5,100,000 $4,800,000 Long Term Debt $17,800,000 $20,916,650Total Liabilities $80,182,600 $75,670,914 Shareholder's Equity: Stock$2,000,000 $2,000,000 Market Equity $27,123,116 $18,273,698 RetainedEarnings $15,342,410 $29,044,173 Future Earnings $270,150,000$222,450,000 Total Shareholder's Equity $314,615,526 $271,767,871 TotalLiabilities & $394,798,126 $347,438,785 Shareholder Equity

[0214] After the report bots are initialized by the software in block504 processing passes to a block 505. In block 505 the bots activate inaccordance with the frequency specified by the user (20) in the systemsettings table (140). After being activated, the bots retrieveinformation for the organization, enterprise or element of value fromthe element of value definition table (155), the component of valuedefinition table (156) and the real option value table (1) as requiredto complete the report in accordance with the pre-specified format. Theresulting reports are then saved in the report table (164) in theapplication database (50). The software in block 505 creates anddisplays all Value Map® reports and Value Creation Statement reports theuser (20) requests using the report selection and display data window(705) in the general format shown in Table 41. Graphical reports such asthose in a Hyperbolic Tree format that have been saved over time can bedisplayed like a “movie” shows the evolution of value over time. Thesoftware in block 505 also prompts the user (20) using the reportselection and display data window (705) to select reports for printing.After the user's input regarding reports to print has been stored in thereports table (164), processing advances to block 507. If the userdoesn't provide any input, then only the default reports specified bythe user (20) in the system settings table (140) will be produced forstorage.

[0215] The software in block 507 checks the reports tables (164) todetermine if any reports have been designated for printing. If reportshave been designated for printing, then processing advances to a block506. The software in block 506 sends the designated reports to theprinter (118). After the reports have been sent to the printer (118),processing advances to a software block 509. Alternatively, if noreports were designated for printing then processing advances directlyfrom block 507 to block 509.

[0216] The software in block 509 checks the system settings table (140)in the application database (50) to determine if trading in enterpriseequity is authorized. If trading in enterprise equity is not authorized,then processing advances to a software block 507. Alternatively, iftrading in enterprise equity is authorized, then processing advances toa software block 510.

[0217] The software in block 510 retrieves information from the systemsettings table (140) and the advanced finance system table (147) that isrequired to calculate the minimum amount of cash that will be availablefor investment in enteprise equity during the next 12 month period. Thesystem settings table (140) contains the minimum amount of cash andavailable securities that the user (20) indicated was required forenterprise operation while the advanced finance system table (147)contains a forecast of the cash balance for the enterprise for eachperiod during the next 12 months. After the amount of available cash foreach enterprise is calculated and stored in the equity purchase table(165), processing advances to a software block 511. The software inblock 511 checks the equity purchase table (165) and enterprisesentiment table (166) to see if there is negative sentiment in anyenterprise with available cash. If there are no enterprises withnegative sentiment and available cash, then processing advances asoftware block 602. Alternatively, if there are enterprises withavailable cash and negative sentiment, then processing advances to asoftware block 512.

[0218] The software in block 512, retrieves the current enterpriseequity price from the external database table (146), calculates thenumber of shares that can be purchased using the available cash and thengenerates a purchase order for the number of shares that can bepurchased. The software in block 512 then prompts the user (20) via thepurchase shares and confirm data window (706) to confirm the purchase.Once the user (20) confirms the equity purchase, the software in block512 retrieves the on-line equity account information from the systemsettings table (140) and transmits and confirms the order to purchasethe shares with the on-line broker via the network (45). The details ofequity purchase transaction and confirmation are saved in the equitypurchase table (156) before processing advances to block 602.

GENERATE AND ANALYZE VALUE IMPROVEMENTS

[0219] The flow diagram in FIG. 9 details the processing that iscompleted by the portion of the application software (600) thatgenerates and analyzes value improvements. Processing in this portion ofthe application starts in software block 602.

[0220] The software in block 602 checks the system settings table (140)in the application database (50) to determine if the current calculationis a new calculation or a structure change. If the calculation is not anew calculation or a structure change, then processing advances to asoftware block 606. Alternatively, if the calculation is new or astructure change, then processing advances to a software block 603.

[0221] The software in block 603 checks the bot date table (149) anddeactivates any improvement bots with creation dates before the currentsystem date. The software in block 603 then retrieves the informationfrom the system settings table (140), the soft asset system table (148),the element of value definition table (155) and the component of valuedefinition table (156) as required to initialize improvement bots beforeprocessing advances to a block 604.

[0222] Bots are independent components of the application that havespecific tasks to perform. In the case of improvement bots, theirprimary task is to analyze and prioritize potential changes to valuedrivers for each enterprise in the organization. The analysis of valuedriver changes closely mirrors the calculation of profit improvementthat was completed in the related U.S. Pat. No. 5,615,109 a “Method ofand System for Generating Feasible, Profit Maximizing Requisition Sets”.The capital efficiency of the potential improvements identified by theimprovement bots is evaluated in accordance with the formula shown inTable 43. TABLE 43 Capital Change (+) Capital Change (−) CapitalRevenueΔ − ExpenseΔ RevenueΔ − ExpenseΔ − efficiency Capital Δ Capital ΔWhere: Revenue Δ = revenue impact of 1% change in value driver Expense Δ= expense impact of 1% change in value driver Capital Δ = capital impactof 1% change in value driver

[0223] The software in block 604 generates a list of potentialimprovements for each element of value defined and measured by thesystem of the present invention.

[0224] Every improvement bot contains the information shown in Table 44.TABLE 44 1. Unique ID number (based on date, hour, minute, second ofcreation) 2. Creation date (day, hour, minute, second) 3. Mappinginformation 4. Storage location 5. Element of ValueID 6. Soft AssetSystem 7. Value Driver

[0225] After the improvement bots are initialized by the software inblock 603 processing passes to a block 604. In block 604 the botsactivate in accordance with the frequency specified by the user (20) inthe system settings table (140). After being activated, the botsretrieve information for the element of value from the system settingstable (140), the soft asset system table (148), the element of valuedefinition table (155) and the component of value definition table (156)as required to complete the analyses in accordance with the formulashown in Table 40. The soft asset management system that corresponds tothe element of value being analyzed may also have generated a list ofpotential improvements. If it has generated a list, these improvementsare analyzed in the same manner that the improvements generated by thesystem of the present invention are analyzed. The resulting list ofprioritized improvements are then saved in the value driver change table(167) in the application database (50) before processing advances to ablock 605.

[0226] The software in block 605 prepares a list of the potential valueimprovements in capital efficiency order and prompts the user (20) via avalue driver and structure change window (707) to modify and/or selectthe improvements and/or structure changes that should be included in therevised forecast. If the user (20) chooses not to enter any selections,then the software in block 605 will select the potential improvementsthat produce the most benefit within the constraints imposed by theavailable cash. The information regarding the improvement selectionsmade by the user (20) or the system are stored in the value driverchange table (167) in the application database (50). In a similarfashion, if the user made any changes to the structure, the informationregarding the new change is stored in the system settings table (140)before processing advances to a software block 606.

[0227] The software in block 606 checks the system settings table (140)in the application database (50) to determine if the current calculationis a structure change. If the calculation is new or a structure change,then processing advances to software block 204 and the processingdescribed above is repeated. Alternatively, if the calculation is not astructure change, then processing advances to a software block 610.

[0228] The software in block 610 retrieves information from the systemsettings table (140), the element of value definition table (155), thecomponent of value definition table (156) and the value driver changetable (167) as required to define and initialize a probabilisticsimulation model. The preferred embodiment of the probabilisticsimulation model is a Markov Chain Monte Carlo model, however, othersimulation models can be used with similar results. The informationdefining the model is then stored in the simulation table (168) beforethe software in block 610 iterates the model as required to ensure theconvergence of the frequency distribution of the output variables. Afterthe simulation calculations have been completed, the software in block610 saves the resulting information in the simulation table (168) beforedisplaying the results of the simulation to the user (20) via a ValueMentor™ Reports data window (708) that uses a summary Value Map™ reportformat to display the mid point and the range of estimated future valuesfor the various elements of each enterprise and the changes in valuedrivers, user-specified or system generated, that drove the future valueestimate. The user (20) is prompted to indicate when the examination ofthe displayed report is complete and to indicate if any reports shouldbe printed. If the user (20) doesn't provide any information regardingreports to display or print, then no reports are displayed or printed atthis point and system processing continues. The information entered bythe user (20) is entered in to the report table (164) before processingadvances to a block 611.

[0229] The software in block 611 checks the reports tables (164) todetermine if any additional reports have been designated for printing.If additional reports have been designated for printing, then processingadvances to a block 612 which prepares and sends the designated reportsto the printer (118). After the reports have been sent to the printer(118), processing advances to a software block 614. If the software inblock 611 determines that no additional reports have been designated forprinting, then processing advances directly to block 614.

[0230] The software in block 614 checks the system settings table (140)in the application database (50) to determine if the current calculationis a continuous calculation. If the calculation is a continuouscalculation, then processing advances to software block 204 where theprocessing described previously is repeated continuously. Alternatively,if the calculation is not continuous, then processing advances to asoftware block 615 where processing stops.

[0231] Thus, the reader will see that the system and method describedabove transforms extracted transaction data, corporate information andinformation from the internet into detailed valuations for anorganization, the enterprises in the organization and for specificelements of value within the enterprise. The level of detail containedin the business valuations allows users of the system to monitor andmanage efforts to improve the value of the business in a manner that issuperior to that available to users of traditional accounting systemsand business valuation reports.

[0232] While the above description contains many specificity's, theseshould not be construed as limitations on the scope of the invention,but rather as an exemplification of one preferred embodiment thereof.Accordingly, the scope of the invention should be determined not by theembodiment illustrated, but by the appended claims and their legalequivalents.

1. Independent software components that extract and store organizationrelated data in accordance with a common schema defined by xml metadatato support organization processing.
 2. The software components of claim1 where an organization is a single product, a group of products, adivision, a company, a multi-company corporation or a value chain. 3.The software components of claim 1 where the data is stored in tables.4. The software components of claim 1 where the common schema includesan organization designation.
 5. The software components of claim 1 wherethe common schema includes a data dictionary.
 6. The software componentsof claim 1 where the data dictionary defines standard data attributesfrom the group consisting of account numbers, components of value,currencies, elements of value, units of measure and time periods.
 7. Thesoftware components of claim 1 where organization related data isobtained from the group consisting of advanced financial systems, basicfinancial systems, alliance management systems, brand managementsystems, customer relationship management systems, channel managementsystems, estimating systems, intellectual property management systems,process management systems, supply chain management systems, vendormanagement systems, operation management systems, enterprise resourceplanning systems (ERP), material requirement planning systems (MRP),quality control systems, sales management systems, human resourcesystems, accounts receivable systems, accounts payable systems, capitalasset systems, inventory systems, invoicing systems, payroll systems,purchasing systems, web site systems, external databases andcombinations thereof.
 8. The software components of claim 1 where atleast a portion of the data is from the Internet or an externaldatabase.
 9. The software components of claim 1 that convert data tomatch the common schema as required.
 10. The software components ofclaim 1 that support processing for organization analysis.
 11. Networkmodels for aspects of organization financial performance that supportorganization analysis, management and optimization.
 12. The networkmodels of claim 11 that are selected from the group consisting of modelsthat quantify the impact of sub elements of value on the elements ofvalue, models that quantify the impact of elements of value onenterprise value, models that quantify the impact of each enterprise onorganization value, two tiered models that quantify the impact of subelements of value on the elements of value and the impact of elements ofvalue on enterprise value, two tiered models that quantify the impact ofelements of value on enterprise value and the impact of each enterpriseon organization value and three tiered models that quantify the impactof sub elements of value on the elements of value, the impact ofelements of value on enterprise value and the impact of each enterpriseon organization value.
 13. The network models of claim 12 where theinputs to the network models are selected from the group consisting oftangible indicators of element impact, combinations of tangibleindicators of element impact and combinations thereof.
 14. The networkmodels of claim 12 where the impacts on elements of value, enterprisevalue and organization value are identified by category of value wherethe categories of value are selected from the group consisting ofcurrent operation, real options, market sentiment and combinationsthereof.
 15. The network models of claim 14 where the current operationcategory of value can be further subdivided by component of value wherecomponents of value are selected from the group consisting of revenue,expense, capital change and combinations thereof.
 16. The network modelsof claim 12 where the hidden layer in the network models quantify therelationship between each input, the other inputs and the outputmeasure.
 17. The network models of claim 12 where the elements of valueare selected from the group consisting of alliances, brands, channels,customers, customer relationships, employees, employee relationships,intellectual capital, intellectual property, partnerships, processes,production equipment, supply chain, vendors, vendor relationships andcombinations thereof.
 18. The network models of claim 12 where thesubelements of value are selected from the group consisting of a singlealliance, groups of alliances, a single brand, groups of brands, asingle customer, groups of customers, a single customer relationship,groups of customer relationships, a single employee, groups ofemployees, a single employee relationship, groups of employeerelationships, a single piece of intellectual property, groups ofintellectual property, a single partnership, groups of partnerships, asingle process, groups of processes, a single vendor, groups of vendors,a single vendor relationship, groups of vendor relationships andcombinations thereof.
 19. The network models of claim 11 that supportorganization analysis, management and optimization activities from thegroup consisting of automated equity trading, contribution analysis,element ranking, impact analysis, management reporting, multi-criteriaoptimization, network optimization, option discount rate calculation,pricing optimization, process optimization, purchasing optimization,simulation, element valuation, closed loop optimization and combinationsthereof.
 20. The network models of claim 11 that are developed bylearning from the data.
 21. The network models of claim 20 where thelearning is completed on a continuous basis.
 22. The network models ofclaim 11 that are selected from the group consisting of neural networkmodels, bayesian models, regression models, multi-adaptive regressionspline models and combinations thereof.
 23. The network models of claim11 where the aspects of organization financial performance are selectedfrom the group consisting of revenue, expense, capital change, marketsentiment, cash flow and market value.
 24. A computer readable mediumhaving sequences of instructions stored therein, which when executedcause the processors in a plurality of computers that have beenconnected via a network to perform an organization share price method,comprising: integrating organization related data in accordance with acommon schema, developing a model of organization share price thatidentifies the value impact of each element of value using at least aportion of said data, and identifying a trading price for organizationshares using said model.
 25. The computer readable medium of claim 24where the value impact of each element is the product of the relativeelement contributions to each category of value and the value of thecategories of value where the categories of value are selected from thegroup consisting of current operation, real option, market sentiment andcombinations thereof.
 26. The computer readable medium of claim 24 wherethe common schema further comprises a schema defined in accordance withan xml metadata standard.
 27. The computer readable medium of claim 24where the method further comprises: completing one or more organizationequity transactions based on the difference between market price and thetrading price in an automated fashion.
 28. The computer readable mediumof claim 27 where the share trading price is the price where the valueof organization market sentiment is negative.
 29. The computer readablemedium of claim 24 where the method further comprises: displaying thevalue impacts for each of one or more elements of value using a paperdocument or electronic display.
 30. The computer readable medium ofclaim 29 where the elements of value are selected from the groupconsisting of alliances, brands, channels, customers, customerrelationships, employees, employee relationships, intellectual capital,intellectual property, partnerships, processes, production equipment,supply chain, vendors, vendor relationships and combinations thereof.31. The computer readable medium of claim 24 where the method furthercomprises: identifying a list of changes in indicators of element impactthat will optimize one or more aspects of organization financialperformance using said model, and displaying the list of changes and theorganization value after the changes.
 32. The computer readable mediumof claim 31 where the elements of value are selected from the groupconsisting of alliances, brands, channels, customers, customerrelationships, employees, employee relationships, intellectual capital,intellectual property, partnerships, processes, production equipment,vendors, vendor relationships and combinations thereof.
 33. The computerreadable medium of claim 31 where the indicators of element impact areselected from the group consisting of composite variables, transactionaverages, time lagged transaction averages, transaction ratios, timelagged transaction ratios, transaction trends, time lagged transactiontrends, time lagged transaction data, transaction patterns, time laggedtransaction patterns, geospatial measures, time lagged geospatialmeasures, relative rankings, links, frequencies, time periods, averagetime periods, cumulative time periods, rolling average time periods,cumulative total values, the period to period rates of change andcombinations thereof.
 34. The computer readable medium of claim 31 whereaspects of organization financial performance are selected from thegroup consisting of revenue, expense, capital change, current operationvalue, real option value, market sentiment value, market value andcombinations thereof.
 35. The computer readable medium of claim 24 whereorganization related data are obtained from the group consisting ofadvanced financial systems, basic financial systems, alliance managementsystems, brand management systems, customer relationship managementsystems, channel management systems, estimating systems, intellectualproperty management systems, process management systems, supply chainmanagement systems, vendor management systems, operation managementsystems, enterprise resource planning systems (ERP), materialrequirement planning systems (MRP), quality control systems, salesmanagement systems, human resource systems, accounts receivable systems,accounts payable systems, capital asset systems, inventory systems,invoicing systems, payroll systems, purchasing systems, web sitesystems, external databases and combinations thereof.
 36. The computerreadable medium of claim 24 where the data includes historical data,forecast data and combinations thereof.
 37. The computer readable mediumof claim 24 where the data are transaction data, descriptive data,geospatial data, text data, linkage data and combinations thereof. 38.The computer readable medium of claim 24 where an organization is asingle product, a group of products, a division, a company, amulti-company corporation or a value chain.
 39. The computer readablemedium of claim 24 that identifies and analyzes the factors that have aneffect on facets of organization financial performance where the facetsare selected from the group consisting of intellectual capital, elementsof value, components of value, categories of value and combinationsthereof.
 40. Independent software components that integrate organizationrelated data from a plurality of sources using a common data dictionaryto support organization processing.
 41. The software components of claim40 where an organization is a single product, a group of products, adivision, a company, a multi-company corporation or a value chain. 42.The software components of claim 40 where the data dictionary comprisespart of an xml schema.
 43. The software components of claim 40 where thedata dictionary defines standard data attributes from the groupconsisting of account numbers, components of value, currencies, elementsof value, units of measure and time periods.
 44. The software componentsof claim 40 where organization related data is obtained from the groupconsisting of advanced financial systems, basic financial systems,alliance management systems, brand management systems, customerrelationship management systems, channel management systems, estimatingsystems, intellectual property management systems, process managementsystems, supply chain management systems, vendor management systems,operation management systems, enterprise resource planning systems(ERP), material requirement planning systems (MRP), quality controlsystems, sales management systems, human resource systems, accountsreceivable systems, accounts payable systems, capital asset systems,inventory systems, invoicing systems, payroll systems, purchasingsystems, web site systems, external databases and combinations thereof.45. The software components of claim 40 where at least a portion of thedata is from the Internet or an external database.
 46. The softwarecomponents of claim 40 that convert data to match the common datadictionary as required.
 47. The software components of claim 40 thatsupport processing for organization analysis.
 48. The softwarecomponents of claim 40 that support processing for organizationmanagement.
 49. The software components of claim 40 that supportprocessing for organization optimization.